Table of contents

About 60 years after the discovery of the Josephson effect, electrical DC voltage calibrations are routinely performed worldwide—mostly using automated Josephson voltage standards (JVSs). Nevertheless, the field of electrical quantum voltage metrology is still propagating towards AC applications. In the past 10 years the fabrication of highly integrated arrays containing more than 50 000 or even 300 000 junctions has achieved a very robust level providing highly functional devices. Such reliable Josephson arrays are the basis for many novel applications mainly focussing on precision AC measurements for signal frequencies up to 500 kHz. Two versions of quantum AC standards are being employed. Programmable JVS, based on series arrays divided into subarrays, reach amplitudes up to 20 V and usually are used as quantum voltage reference in measurement systems. Pulse driven arrays reach amplitudes up to 1 V or even 4 V and are typically used as Josephson arbitrary waveform synthesizers. This paper summarizes the principal contributions from Physikalisch-Technische Bundesanstalt to the present state of JVS with particular focus on developments for precision metrological applications and our proof-of-concept demonstrations.

The field of optical 3D metrology is gaining significant interest in the past years. Optical sensors can probe the geometry of workpieces and biological samples very fast, highly accurate and without any tactile physical contact to the object's surface. In this respect, optical sensors are a pre-requisite for many applications in the big trends like Industrial Internet of Things, Industry 4.0 or Medicine 4.0. The interest for optical 3D metrology is shifting from a metrology for quality assurance in industrial production to "digitize the real world" to facilitate a precise digital representation of an object or an environment for documentation or as input data for virtual applications like digital fab or augmented reality. The aspiration to digitize the world necessitates fast and efficient contact free sensing principles of appropriate accuracy for solid and even soft objects with a variety of colour, surface texture and lighting conditions. This review article tries to give a concise conceptual overview about the evolution of a broad variety of optical measurement principles that evolved and gained some importance in the field of 3D metrology for industrial 3D applications and their related technological enablers.

Floating offshore wind turbines (FOWTs) can harvest more wind energy in deep water. However, due to their complex mechanical structure and harsh working conditions, various sensors, actuators, and components of FOWTs can malfunction and fail. To avoid serious accidents and reduce operation and maintenance costs, fault detection plays a critical role in wind-energy engineering, particularly for offshore wind energy. Because of complex characteristics, such as dynamics and nonlinearity, an accurate mathematical model cannot be easily obtained from first principles for FOWTs. In this paper, a new data-driven fault-detection method based on kernel canonical variable analysis (KCVA) is proposed for FOWTs. In the proposed method, the collected measurements are first augmented into time-lagged variables to capture the dynamics of FOWTs. The time-lagged variables are then mapped to a high-dimensional feature space to extract nonlinear features. Specifically, canonical variable analysis (CVA) is carried out to explore the correlations in high-dimensional feature space. For fault detection, two monitoring indexes including T² and squared prediction error ($\mathrm{SPE}$) statistics are established. To verify the performance of the proposed KCVA-based fault-detection method, experiments on a high-fidelity FOWT benchmark, which was created from the National Renewable Energy Laboratory Fatigue, Aerodynamics, Structures, and Turbulence v8.0 simulator, were carried out. The results show the capability and efficiency of the proposed KCVA-based fault-detection method in comparison with other related methods.

Recently, the combination of pattern recognition technology and distributed fiber sensing systems has become increasingly common, so whether the disturbance signal can be well recovered has become increasingly important. To verify the recovery and linear response of a distributed fiber optic sensing system to multi-frequency disturbance signals, a heterodyne coherent detection system for phase-sensitive optical time-domain reflectometry is developed. The output beat signal is extracted using the digital in-phase/quadrature demodulation algorithm. The signal can be precisely located on a 7 km length range, and the disturbance signal can be restored well through the phase information. Not only the superposition signal composed of the same signal but also that composed of different kinds of signals can be successfully restored. A fast Fourier transform algorithm is used to obtain the frequency information of the superimposed signal. Combined with the use of a finite impulse response filter, the superposed signal is decomposed according to its frequency components, which perfectly restores the two signals before they are superimposed. In addition, their amplitude is highly linear with the driving voltage of the piezoelectric transducer. The system can fully retain the details of each frequency component in the recovery of multi-frequency disturbance signals. More importantly, in field experiments, the disturbance behavior is well recovered, which has broad prospects in the application of perimeter security.

During the initial stages of the COVID-19 pandemic a major concern was the shortage of ventilators. Engineering initiatives were started to mitigate this concern. This article details the response at the National Physical Laboratory (NPL) to this potential shortage. The technologies and product development behind NPL's PocketVent ventilator, by a team working remotely amidst a crisis, are explored. Underpinning the product was the creation of a traceable test facility, that allowed the initial three prototype devices to be narrowed to one. PocketVent was created within five months and offers pressure and volume controlled ventilation, with a clear road-map to implement constant-pressure mode, alongside a comprehensive documentation pack. The team were recipients of the Royal Academy of Engineering's President's Special Award for Pandemic Service.

In this study, a heuristic algorithm is used to find an optimal route for smart logistics loading and unloading applications. Various environments, such as traditional building blocks, satellite images, terrain environments, and Google map environments are developed by converting into a binary occupancy grid and used to optimize the viable path in the smart mobile logistics application. The proposed autonomous vehicle (AV) route planning navigation approach is to forecast the AV's path until it detects an imminent obstacle, at which point it should turn to the safest area before continuing on its route. To demonstrate the path navigation results of proposed algorithms, a navigational model is developed in the MATLAB/Simulink 2D virtual environment. The particle swarm optimization (PSO) method, the Bat search algorithm, and its proposed variants are used to identify a smooth and violation-free path for a given application environment. The proposed variants improve the algorithm's effectiveness in finding a violation-free path while requiring less time complexity by using cubic spline curve interpolation and its improved constriction factor. Extensive simulation and benchmark validation results show the proposed standard PSO has a significantly shorter violation-free path, quick convergence rate and takes less time to compute the distance between loading and unloading environment locations than the cooperative coevolving PSO, Bat algorithm, or modified frequency Bat algorithms.

Remaining useful life prediction based on degraded data is the premise of intelligent maintenance of equipment. Since the degradation process is usually complex and changeable, the general deep learning prediction method brings a large prediction deviation since it uses linear target function. In this paper, temporal convolutional network combined with a nonlinear target function (NT-TCN) is proposed to improve the prediction accuracy. The nonlinear target function is constructed using a piecewise function to label degraded data. By means of dilated causal convolution and Mish activation function, the NT-TCN learns the time series characteristics of degeneration. Thus, high precision of life prediction is achieved. By analyzing NASA's C-MAPSS data set, the prediction results show that the NT-TCN model has lower root mean squared error and evaluation score than the neural network model with the linear target function, which is also superior to some existing prediction methods.

The vibration signal contains a lot of state information, and its time domain, frequency domain, and time-frequency domain features are extracted to describe the rotor's operational state in detail. However, multi-domain and multi-angle feature extraction can lead to information redundancy, causing the 'dimensionality catastrophe' problem, which also makes fault classification more difficult. Dimensionality reduction (DR) is a technique that can help with this issue, but the majority of current DR algorithms are based on a simple intrinsic structure. To accurately characterize the intrinsic structure of high-dimensional fault dataset, this study proposed a novel DR algorithm that considers local information, global information, and the hypergraph model, named local–global standard hypergraph embedding (LGSHE). LGSHE redefines the hypergraph weight matrix's calculation formula and constructs the local intra-class standard hypergraph, local inter-class standard hypergraph, global intra-class standard hypergraph, and global inter-class standard hypergraph to characterize the fault data structure. LGSHE can accurately characterize the intrinsic structure of high-dimensional fault dataset and increase fault classification accuracy. The performance of LGSHE is validated on two different structures of double-span rotor experimental benches, and the results show that the algorithm can achieve good fault classification accuracy.

To solverole inefficiency and complete autonomy issues for deflection of the vertical (DOV) determination in geomatics, navigation, and military weapon launching fields, a calculation method based on path compensation has been proposed. After conducting the discretization of the Vening-Meinesz formula in the spherical surface, a novel path gravity anomaly compensation based on the 'remove–restore' algorithm was formulated to satisfy the requirement of regional high-precision gravity anomaly data. Meanwhile, utilizing the European Improved Gravity model of Earth by New techniques (EIGEN-6c4), the DOV was calculated using the Vening-Meinesz formula based on the 'remove–restore' method, unlike the previous regional direct integral calculations. Finally, to verify the reliability and feasibility of the algorithm, the gravity measurement system, consisting of the high-precision gravimeter and strapdown inertial navigation system, was constructed to conduct the simulation and shipborne experiments. The simulation and experiment results of the proposed method show that the root mean squares, relative to the real values, of DOV calculation components, are 0.89'' and 1.67'', while standard deviation values of the DOV components are the same within 2'' in experiments, which satisfies the precision requirement of the DOV.

Noise source identification of gas turbines can provide the basis and guidance for vibration and noise reduction of gas turbines. Independent component analysis (ICA) is one of the most popular techniques for blind source separation (BSS) widely used in vibration and noise source separation in mechanical systems. ICA is suitable for independent source signals. However, in order to identify dependent mechanical noise sources in gas turbines, a convolutive BSS in the frequency domain based on bounded component analysis (BCA) is proposed. First, the basic theory of BSS and BCA is introduced in detail. The convolutive mixing in the time domain is transformed into an instantaneous mixing in the frequency domain by short time Fourier transform (STFT), and complex BCA is performed at each frequency bin. Second, a permutation alignment method based on local and global optimization is proposed to solve the problem of the permutation ambiguity. Finally, the accuracy and robustness of the proposed method are comparatively studied through typical numerical and experimental studies on a three-rotor experimental bench. The results show that the proposed method can effectively separate and identify independent and dependent source signals.

Temperature-variation-induced error compensation plays a pivotal role in large-scale metrology, in which the nonuniform temperature field would greatly influence the coordinate transformation process between different systems. This paper proposes a novel analytical thermal deformation compensation method with spatial discretisation modelling among 2D distributed enhanced reference system points to reduce the thermal influence on the coordinate transformation process. First, the analytical thermal deformation model based on the spatial discretisation is established with the plane thermal elastic mechanics and the Finite Difference Method. Then the nonuniform temperature field model is developed based on Ordinary Kriging spatial interpolation. Finally, a compensation method for the coordinate transformation process is proposed based on the actual measurement data and the proposed thermal deformation model. The experimental results illustrate the existence and significance of the nonuniform feature of the temperature field. By the proposed compensation method, the coordinate transformation error is reduced by 47.13% and 76.67% in the x and y directions, respectively. The method is applicable to diminish the influence of thermal deformation under a nonuniform temperature field on the coordinate transformation of large-scale metrology.

This paper investigates an effective method for measuring the thickness of highly attenuating materials using the acoustic radiation-induced quasi-static component (QSC) of a primary longitudinal wave (PLW) at high frequency. The generated QSC features lower attenuation than the high-frequency PLW, so the generated QSC pulse with zero carrier frequency can propagate a longer distance at the same group velocity, even in highly attenuating materials. In addition, the method based on the QSC of a high-frequency PLW has better directivity than the low-frequency PLW-based method, making it more suitable for highly attenuated material local thickness measurement. The thickness of highly attenuating materials can be accurately measured by measuring the pulse-echo time-of-flight of the generated QSC pulse using an ultrasound pulse-echo technique. The experimental examinations conducted for highly attenuating silicone rubber blocks with different thicknesses demonstrate that their thicknesses can be accurately measured with the QSC-based method. This paper provides an effective method for thickness measurements of highly attenuating materials.

Affected by dynamic changes in the complex marine environment, ultrashort baseline (USBL) systems may exhibit continuous gross errors in underwater target positioning, resulting in the distortion of the target coordinates. To effectively detect and eliminate continuous gross errors in USBL underwater acoustic positioning, a robust sequential adaptive Kalman filter (RSAKF) algorithm is proposed in this paper. The RSAKF algorithm employs sequential filtering to decompose all measurement updates into multiple submeasurement updates and uses the fading memory weighted average method to estimate the one-step prediction mean square error of the metrics for each submeasurement update. Then, the RSAKF algorithm adopts an adaptive correction method of submeasurement noise variance, which eliminates the influence of continuous gross errors through a more targeted adaptive correction of each submeasurement noise variance. The effectiveness of the algorithm was quantitatively analyzed using a USBL positioning simulation experiment, and the results showed that the continuous gross errors rejection rate of the RSAKF algorithm reached 84.12%. The point error of the RSAKF algorithm is improved by 62.65%, 46.76%, 36.09%, and 26.48% compared with the Kalman filter (KF), KF based on Huber, KF based on Institute of Geodesy and Geophysics, and the maximum correntropy KF, respectively. The USBL positioning remotely operated vehicle experiment was conducted in the South China Sea, and the results showed that the RSAKF has the best filtering accuracy. Simulation and actual measurement experiments verified that the RSAKF algorithm can effectively eliminate the influence of continuous gross errors.

Deep strong-supervised learning-based methods have been widely used and have made significant progress in intelligent fault diagnosis for wind turbine (WT) gearboxes. The superior performance of such methods relies on high-quality labels. However, correctly labeling the data is challenging because of the complexity of fault vibration signals and fault modes in real industrial scenarios, resulting in noisy labels in datasets, which significantly restricts the application of strong-supervised fault diagnosis models. In this study, a method based on twin prototype networks with noisy label self-correction was proposed to address fault diagnosis for WT gearboxes with noisy labels. This method introduced a collaborative learning architecture to improve the confirmation bias in the self-training of individual networks and to slow the speed of learning noisy-labeled samples. Simultaneously, the loss distribution of the samples from each network was modeled using the Gaussian mixture model to dynamically identify mislabeled samples in the training dataset. Finally, a collaborative relabeling prototype refinement module was designed to optimize the prototype learning process and enable self-correction of noisy labels. The experiments demonstrated the effectiveness and superiority of the proposed method.

Deep learning (DL) fault diagnosis methods require no expert knowledge and can adaptively extract fault features to realize automated diagnoses. However, factories' limited and imbalanced data cause DL fault diagnosis methods to fail to meet data diversity requirements and perform poorly. To solve this problem, this paper proposes triple Wasserstein generative adversarial nets with classifier penalty (Triple-WGAN-CP). We first train Triple-WGAN-CP to generate samples to balance the original unbalanced dataset, then input the new balanced dataset to the fault classifier of Triple-WGAN-CP to continue training. Finally, when the numbers of consecutive sampling points in each of the nine fault classes are only 3140, 2300, and 2076, we achieve the highest prediction accuracies of 99.5%, 95.1%, and 65.1%, respectively, and the highest average accuracies for the nine environments (signal-to-noise ratio −4, −2, 0, 2, 4, 6, 8, 10, and ∞) of 96.2%, 84.1%, and 55.1%, respectively. Comparisons with other methods show that this has achieved significant improvements in accuracy and noise robustness and has broad application prospects in the field of limited and imbalanced data fault diagnosis.

In this paper, we propose an unbalance parameters identification method based on a time-domain response that combines a matrix equation and spectral correction technique (SCT). Numerical simulations and experiments are conducted to demonstrate the performance of the proposed method. A commonly used method for identifying unbalance parameters based on time-domain response is the equivalent load method, in which the modal expansion technique (MET) is used to extend the vibration response of a few of measurement points to the vibration response of all degrees of freedom of the model. However, this technique may result in incorrect identification. To solve the above problem, we first directly derive a matrix equation describing the relationship between the vibration response at the measurement point and the unbalanced forces based on the state space equations of the reduced-order model. This process avoids the application of METs and effectively improves the robustness of the identification results. The unbalanced force can then be obtained based on the vibration response by solving the matrix equation using the regularisation method. Finally, the unbalance parameters are obtained by processing the unbalanced force using the SCT. The simulation and experimental results show that, compared with the traditional equivalent load method, the proposed method can identify the unbalance parameters with higher accuracy, which proves its application prospect in balancing machines and field balancing.

The monitoring performance of receiver autonomous integrity monitoring (RAIM) is restricted when visible satellites are limited in challenging environments. For that, artificial neural network-based RAIM methods have been investigated to improve the detection efficacy. Nevertheless, their corresponding fault exclusion and protection level algorithms are hardly provided for integrity assessments. In this regard, a nonparametric estimation-based RAIM method (NE-RAIM) is investigated to support fault detection, exclusion, and protection level calculation in this paper, boosting the declined monitoring capacity caused by the decrease of visible satellites. We propose a classification variable and a dynamic sampling method based on the variance inflation theory and then obtain the regression of the classification variable using nonparametric estimation. In this way, a five-layer NE-RAIM neural network is constructed to enhance the detection capability further. We also provide a NE-RAIM-based fault exclusion strategy by analyzing the detection result vector. Meanwhile, a protection level algorithm is proposed to enable direct integrity and availability evaluation based on searching the worst-case scenario where the missed detection risk is maximized. Results show that NE-RAIM requires a minimum pseudorange bias of 35 m to realize 100% detection rates under all single-faulty-satellite modes. Compared with least-square RAIM and advanced RAIM, NE-RAIM improves overall 24 h availability by 59.30% and 4.52%, respectively.

Binocular vision measurement benefits from high prediction robustness and low structural complexity. However, there are still significant flaws in its accuracy. In this paper, binocular vision measurement of a rectangular workpiece is investigated. A new precise measurement method based on binocular vision is designed to achieve precise measurement of rectangular workpiece dimensions. Firstly, an algorithm for workpiece location based on Zernike moments and corner matching is proposed and employed to precisely locate the workpiece and extract the sub-pixel coordinates of discrete points on an image's edge. Then, a novel stereoscopic matching algorithm combined with epipolar-geometry and cross-ratio invariance (CMEC) is proposed to improve the accuracy of binocular vision stereoscopic matching. Finally, a projection plane is introduced after the 3D reconstruction of discrete points in the workpiece contours by fitting the plane with least squares. The projection plane limits the coordinate fluctuations of discrete points. Furthermore, the data screening is used to further improve the accuracy of size calculation. The experimental results of the standard checkerboard and actual workpiece show that CMEC's matching accuracy reached 99%, and the proposed method's measurement accuracy reached 0.018 mm. This work presents a novel algorithm for stereoscopic matching in binocular vision and machine vision measurement.

A time-varying moment of inertia (MOI) measurement model based on the torsion pendulum method is studied. According to the principle of dynamics, the motion equation of torsion pendulum with single degree of freedom is established by using the second Lagrange equation modeling, which indicates the time-varying MOI can be obtained by the instantaneous undamped natural frequency of torsional pendulum motion and the torsion bar coefficient. Among them, the instantaneous frequency is calculated by the instantaneous angular frequency and envelope signal of angular displacement using Hilbert transform. The torsion bar coefficient is calibrated by the standard weight with known MOI, and has been compensated by temperature. Based on air floating turntable, the torsion pendulum system is built to measure the MOI of fine sand in the funnel, which can minimize the effects of mechanical friction. The relative error of MOI between calculating the result and measuring the value is less than 0.65%, which verifies the effectiveness of the proposed time varying temperature compensation method.

Motor vibration signal data sets are characteristically random and nonlinear, and its features are difficult to extract for fault identification. To reduce the uncertainty of fault diagnosis, a method based on principal component analysis (PCA) and discrete belief rule base (DBRB) was developed for the first time. Initially, the vibration signal was first denoised using a wavelet threshold algorithm to eliminate interference. Second, overlapping signals were segmented into 15 time windows and a total of 13 typical time domain features and mathematical statistical features were extracted. Third, the dimensions of the features were reduced to three principal components by PCA and were taken as the antecedent attributes of the DBRB. However, the amount of information in each principal component is different, so the variance contribution rate was taken as an antecedent attribute weight to restore the original data characteristics. Fourth, a PCA-DBRB model was established, which effectively avoided the combinatorial explosion problem of rule base in the DBRB model. In addition, to obtain appropriate reference values, the k-means algorithm was introduced to take the cluster centers as reference values. The method was then validated by collecting typical fault data from motor bench experiments. The results demonstrated that compared with other traditional classifiers, this approach is more effective and superior in classification performance and more accurate in diagnosing faults from motor vibration data.

Tubes are widely used in the aerospace field for such things as engines, fuel pipelines, cooling systems, etc. To improve manufacturing efficiency and accuracy, researchers develop multi-camera vision methods for measuring tube geometry parameters. However, these methods cannot measure the exact position of the endpoints of the tube, because the projection points of the tube endpoints have no obvious texture features in the image. To solve the above problem, a precise measurement method for tube endpoints based on spatial geometry and perspective projection model is proposed in this paper. First, we establish a Plücker coordinate system, which treats the end of the tube as a cylinder. Then, several line equations parallel to the center line are calculated on the surface of the cylinder and their intersections with the end face are obtained on these lines, which refers to the optical center of the camera, the radius of the tube, and etc. These intersections are projected onto the centerline and optimized to get the endpoint. Finally, we build a multi-camera vision measurement system consisting of ten industrial cameras to verify the effectiveness of the proposed method. Extensive experiments have shown that the average error of this method is within 0.1 mm, and the standard deviation is within 0.08 mm, which proves the superiority of our proposed method.

Hot bridge wire electroexplosive devices (EEDs) are widely used as starting elements in aerospace and other fields. Their thermal ignition process and safety testing technology have always been a focus of research. In this paper, we construct a temperature rise model for a hot bridge wire EED by using the law of conservation of energy, and the temperature response of the bridge wire and the reagent is obtained. According to the theoretical and numerical analysis, the sensitive criterion for a hot bridge wire EED is given, and the simulation process for the critical ignition current is designed. Based on this process, the law of the influence of the bridge wire and reagent parameters on the ignition of a hot bridge wire EED was studied. The research results show that a first derivative of the reagent temperature with respect to the radial distance of greater than zero is a sensitive criterion for the ignition of a hot bridge wire EED with high accuracy. Compared with traditional numerical methods, the proposed process makes the acquisition of the critical ignition current more precise and programmable due to the use of sensitive criteria. In addition, the following significant features are noted: the critical ignition current first increases as the square root of the bridge wire radius and then linearly; the square of the critical ignition current increases linearly with the activation energy of the reagent; there is rapid exponential decay of the critical ignition current with increasing reagent combustion heat; asymptotic values are present. In engineering use, it is possible to conveniently design the ignition characteristics of a hot bridge wire EED by changing the radius of the bridge wire. This study serves as a foundational work for the safety testing of hot bridge wire EEDs and can provide theoretical guidance for the safety testing, actual production and use of hot bridge wire EEDs.

The fault information of rolling bearings is generally contained in vibration signals. How to efficiently unearth fault information from the raw signals is the key to detecting and evaluating the health condition of mechanical equipment. Therefore, a hierarchical dispersion Lempel–Ziv complexity (HDLZC) feature extraction method is developed in this paper to improve the accuracy of fault diagnosis. In this method, dispersion theory addresses the deficiency of Lempel–Ziv complexity, and can obtain more fault features from the raw signal. Second, the hierarchical extraction of high- and low-frequency components from time series can improve the ability to describe dynamic features. Simulations and experiments respectively demonstrate the predominance of HDLZC. The experimental results reveal that this method is significantly better than multiscale dispersive Lempel–Ziv complexity, hierarchical Lempel–Ziv complexity, multiscale dispersion entropy, and multiscale permutation entropy in extracting fault information.

In the BeiDou navigation satellite system-3 (BDS-3) radio determination satellite service (RDSS), there exists a natural two-way link between the center station and the user site that can be used to achieve time transfer. Traditionally, RDSS two-way time transfer is realized by analogy with the two-way satellite time and frequency transfer (TWSTFT) technique. However, unlike TWSTFT, the signal propagation delays are not completely symmetrical in the RDSS two-way link because of the differences in signal emission time. In this study, a model of two-way time transfer with BDS-3 RDSS is presented. An asymmetric delay compensation method is introduced to solve the asymmetry of RDSS signals and improve the accuracy of two-way time transfer. In addition, BDS-3 RDSS common clock difference experiments are designed to test the performance of two-way time transfer with an asymmetric delay compensation model. Based on analyses of the experimental results, three conclusions can be drawn. First, the accuracy of RDSS two-way time transfer is improved when the asymmetric delay compensation model is applied. Second, BDS-3 RDSS two-way time transfers in different in-station beams and subbands do not show many discrepancies on the whole, and the standard deviation values of the BDS-3 RDSS time transfer results range from 1.7 ns to 2.6 ns for the test signals. Third, the two-way time transfer results are not generally affected by receiver coordinate errors.

The kernel-based geometric learning model has been successfully applied in bevel gearbox fault diagnosis. However, due to its shallow architecture and problems with its sensitivity to noise and outliers, its generalization ability and robustness need to be further improved. Ensemble learning can improve the classification accuracy of sub-classifiers, but it is effective only when the sub-classifiers meet the requirements of difference and accuracy at the same time. However, as strong classifiers, geometric learning models are difficult to produce sub-classifiers with differences. To solve these problems, this study proposes a novel ensemble model, the ensemble convex hull (CH)-based (EnCH) classification model. CH has the advantages of clear geometric meaning and is easy to deform. This paper considers the clustering characteristics of the sample points in the feature space, or both distance and density, and performs differential shrinkage deformation on the original CH. For one thing, this can produce differential CHs to build differential sub-classifiers for the ensemble. Also, it can suppress the interference of noise and outliers to improve robustness. The results of our experiments on the fault dataset of a bevel gear box indicate that the EnCH classification model can improve the generalization of the geometric learning model and has excellent tolerance to noise and outliers.

EN 16516 sets specifications for the determination of emissions into indoor air from construction products. Reliable, accurate and International System of Unit (SI)-traceable measurement results of the emissions are the key to consumer protection. Such measurement results can be obtained by using metrologically traceable reference materials. Gas-phase standards of volatile organic compounds (VOCs) in air can be prepared by a variety of dynamic methods according to the ISO 6145 series. However, these methods are not always applicable for semi-VOCs (SVOCs) due to their high boiling point and low vapour pressure. Therefore, a novel dynamic gas mixture generation system has been developed. With this system gas-phase standards with trace level VOCs and SVOCs in air can be prepared between 10 nmol mol⁻¹ and 1000 nmol mol⁻¹. The VOCs and SVOCs in this study have normal boiling points ranging from 146 °C to 343 °C. Metrologically traceable reference materials of the gas-phase standard were obtained by sampling of the VOC gas-phase standard into Tenax TA^® sorbent material in SilcoNert^® coated stainless steel tubes. Accurately known masses between 10 ng and 1000 ng per VOC were sampled. These reference materials were used to validate the dynamic system. Furthermore, the storage and stability periods of the VOCs in the reference materials were determined as these are crucial characteristics to obtain accurate and SI-traceable reference materials. In a round robin test (RRT), the reference materials were used with the aim of demonstrating the feasibility of providing SI-traceable standard reference values for SVOCs for interlaboratory comparison purposes. Based on the results from the validation, the storage and stability studies and the RRT, gas-phase standards and reference materials of VOCs and SVOCs with relative expanded uncertainties between 5% and 12% (k = 2) have been developed. These reference standards can be used as calibrants, reference materials or quality control materials for the analysis of VOC emissions.

X-ray computed tomography (CT) is considered as one of the most common techniques in non-destructive testing and is also an essential and important method in the field of dimensional measurement. For high-precision measurement task scenarios, geometric errors due to the instability of the CT motion equipment cannot be ignored and need to be accurately measured and corrected. In-situ testing of large objects such as geotechnical materials, mechanical assemblies, metal weldments and concrete specimens also requires high-precision CT imaging of their interiors to obtain information of interest. The rotation of the scanning parts for these large in-situ tests and sparse-view scanning mode also makes it difficult to measure geometric errors. This paper therefore proposes a method for simultaneous measurement of geometric errors based on a priori information for in-situ testing characteristics. This method is capable of measuring geometric errors in sparse view projections with a length accuracy of 0.1 mm and an angular accuracy of $6\times10^{-5}$ radians in 0.5 seconds per angle, as the simulation results show. The reconstruction results show that this level of geometric error measurement accuracy provides a dimensional measurement accuracy of 0.01 mm for the internal structure. The data for the simulation experiments in this paper are generated from real concrete data by noisy simulation.

Chemical oxygen demand (COD) reflects the degree of water damage by organic pollutants, and is an important indicator for water environment protection and marine hydrological monitoring, so it is very important to accurately measure COD. However, changes in ambient temperature and atmospheric noise cause huge deviations in the precise measurement of COD by optical-based water quality detectors. The purpose of this research is to realize the accurate measurement of COD of the optical water quality detector by compensating the environmental parameters of the water quality detector. The compensation model established in this paper is a particle swarm optimization (PSO) back-propagation neural network (BPNN), which can compensate for temperature and filter out atmospheric noise, named back-propagation neural network of particle swarm optimization of filter out atmospheric noise (FAN-PSO-BPNN). FAN-PSO-BPNN reduced the maximum relative error by 92.51%, root mean square error by 91.64%, coefficient of variation by 91.74%, and the distance between the maximum and minimum prediction errors by 92.94% compared with BPNN in filtering out atmospheric noise interference and temperature compensation. The optimization scheme proposed in this paper for BPNN is crucial and effective, and the FAN-PSO-BPNN compensation model improves the anti-interference ability of the water quality detector, guarantees the accurate measurement of COD in seawater, and helps the water quality detector to monitor in seawater for a long time.

The radiation temperature metrology above 150 °C relies heavily on the use of physical interpolation equations and known reference temperature (provided by solid–liquid phase transition of high-purity metals and metal-carbon eutectic alloys) fixed-point blackbodies. Recent achievements in thermal infrared detector technologies triggered the extension of the scale interpolation below this temperature down to 0 °C by using the reference temperatures provided by Sn, In, Ga fixed-points and ice-point, where the reference temperature of the later is dependent on external parameters. In this work, we demonstrate that the triple-point of water (TPW) based fixed-point blackbody is the metrologically grounded alternative to the ice-point. For this purpose, a fixed-point blackbody, incorporating only a cavity and large area TPW (LATPW) cell was designed, constructed, and validated for the precise calibration of radiation thermometers (RTs) and thermal cameras at the thermodynamic temperature of TPW. The conceptual design of the LATPW cells is similar to the ones used in contact thermometry, where a thermometer well of the cell is employed as a borehole for a cavity, where the cavity is easily detachable. Four different cavities (two different designs with aperture sizes of 40 mm and 50 mm) and three LATPW cells with two distinct well geometries were comparatively studied in several combinations. The largest absolute temperature difference observed between the primary level reference TPW cell (used in contact thermometry) and the LATPW cells is measured to be only 0.37 mK. Radiometric measurements demonstrate that all radiators maximally reflect the blackbody condition including emissivity close to unity, high uniformity across the aperture and high temporal stability. The simplicity of maintenance and easy in-field usage (only distilled water and dry ice are required) make the TPW blackbodies very versatile for the in-situ calibrations of RTs and thermal cameras, allowing its application in many areas including clinical environments.

Multiple faults often occur in the operation of rotating machinery transmission systems. The fault signals of multiple bearings interfere with each other, which makes feature extraction and diagnosis of complex compound fault signals difficult. Because the graph convolution networks (GCN) can effectively map the structural information from complex data and its model has a certain generalization ability, this paper proposes a multiple fault diagnosis method for rolling bearings employing complete ensemble empirical mode decomposition (CEEMD) and a GCN (CEEMD-GCN) based on a horizontal visibility graph (HVG). Firstly, in order to highlight the effective feature information in the multiple fault signal and reduce noise interference, multiple indicators of correlation and kurtosis are used to reconstruct the decomposed signals through CEEMD; secondly, the reconstructed signals are constructed as an HVG, and the HVG maps the time series signal to the graphic structure data, reflecting the local geometric characteristics of the vibration signal through the horizontal visibility relationship; finally, taking the signal samples obtained by the HVG algorithm as the input data of the model, the GCN model is trained to realize the diagnosis of multiple faults. The experimental results show that the presented methodology is superior to other methods and exhibits generalization ability for multiple fault diagnosis.

The traditional empirical wavelet transform (EWT) based on the Meyer wavelet and scale-space method can decompose a signal into several empirical modes. However, this method is not effective in dealing with strong noise and non-stationary signals, which may lead to modal mixing or even decompose too many invalid components. For this purpose, a method based on the combination of enhanced empirical wavelet transform (EEWT) and correlation kurtosis (CK) is proposed in this paper. Firstly, the EEWT is used to segment the spectrum based on the characteristics of the spectrum fluctuations. It uses the minimum points of the envelope as the boundaries of the segmented spectrum. Secondly, a filter bank is constructed based on these boundaries and a maximum value order statistics filter segments the Fourier spectrum with the adaptive decomposition of the signals. Finally, the envelope spectrum generated by CK is used to screen the bearing fault information, which belongs to the decomposition of a signal into empirical modes, so that the rolling bearing fault can be accurately diagnosed. The method's effectiveness is verified by simulated signal experiments and rolling bearing fault signals. The results show that the performance of the proposed method in this paper is better than that of the traditional EWT. Therefore, the method can be applied to the field of bearing faults or other mechanical fault diagnosis directions.

Based on BeiDou navigation satellite system (BDS), the Quasi-Zenith satellite system (QZSS) was added to assist in studying the precise point positioning (PPP) time and frequency transfer effect. Ambiguity resolution (AR) is the key to the rapid conversion of the PPP method. Therefore, this paper also used the ionospheric-free combination and the observable-specific signal bias (OSB) product of Wuhan University to test the time–frequency transfer effect of BDS ambiguity-fixed. In this way, BDS PPP, BDS + QZSS PPP, BDS PPP-AR, and global positioning system (GPS) PPP methods were formed. Six stations located in Japan and Australia were selected for experiments. Conclusion: BDS can reach the same level as GPS; when the cut-off angle is greater than 15°, adding QZSS could improve the success rate, accuracy, and frequency stability of the solution of time links effectively; the ambiguity fixed strategy can improve the time transfer accuracy but not the short-term frequency stability.

In solving the problems encountered when implementing support vector machine (SVM) fault diagnosis, the convex-hull-covering model of the dataset cannot reflect the effective distribution of the samples, and the fault identification accuracy of the original high-dimensional feature set is low. A sparse scaling convex-hull based SVM classification method is proposed and applied to the fault diagnosis of roller bearings. The dimensionality reduction of the features of the sample set is carried out by the random forest (RF) algorithm. First, the optimized sample subsets are obtained by sparse approximation, and the reduction coefficient of the convex hull of the optimized sample set is adjusted, hence the convex hulls of various sample sets are linearly separable. Second, to solve the problem of low fault recognition accuracy of the original high-dimensional feature set, the importance of features is evaluated by RF, and some redundant features are removed. Finally, the SVM model is constructed by the closest points between the convex hulls. Through fault diagnosis on two different bearing datasets, the experimental results and related theories show that the proposed method has high performance in bearing fault diagnosis.

Deep learning-based rotating machinery remaining useful life (RUL) prediction approaches rarely consider spatial dependencies and global temporal correlation of monitoring signals simultaneously. Superiorly, graph convolutional networks (GCNs) learn relationship information among graph nodes, considering the spatial dependencies of signals. It is beneficial for constructing high-quality graphs to improve the prediction performance in single-sensor monitoring scenarios. In this paper, an RUL prediction approach for rotating machinery based on a dynamic graph and a spatial–temporal network (STNet) is proposed. Short-time Fourier transform is introduced to obtain node features, and dynamic edge connections are established through node importance weights. Furthermore, an STNet is constructed to learn graph features, in which the GCN is used to mine spatial dependencies of input graphs, and a bi-directional long short-term memory network is applied to capture global temporal correlations. Finally, an autoencoder-based graph readout layer is designed to pass learned graph features. Case studies are conducted to demonstrate its effectiveness.

This paper presents a force/disturbance observer that compensates for the low-frequency response of a piezoelectric force sensor. Although the piezoelectric force sensor has many salient features, it is unable to measure dc and quasi-dc forces. The proposed observer compensates for this low-frequency deficiency, enabling the piezoelectric force sensor to be used in a servo system. Compared with previous studies, the proposed scheme requires redesigning neither the sensor structure nor sensing circuitry and can be easily integrated into an existing measurement system. As a by-product, the proposed observer also produces a disturbance estimate that can be used to enhance the robustness of a servo system. This paper reports experimental results of force control and position control for a linear motion platform equipped with a piezoelectric force sensor and a strain gauge-based load cell. The force control results indicate that the control system using the proposed observer can achieve a wider bandwidth than that using the load cell as a feedback-sensing device. In a comparative study, the position control results further demonstrate the proposed observer's effectiveness in diminishing the piezoelectric sensor's low-frequency deficiency. Moreover, the disturbance estimate produced by the proposed observer is experimentally introduced to the position control system, showing an enhanced tracking performance in terms of the root-mean-square error.

This paper presents a new technique for measurement of current based on magnetic shape memory (MSM) smart alloys. MSM alloys undergo shape changes when exposed to magnetic fields. The non-conventional instrument transformer (NCIT) proposed in this paper utilises this property to measure current. There is a correlation between the magnetic field produced by a current and the shape change of an MSM material (MSM sensor). By exploiting this correlation, we have shown that it is possible to measure alternating currents (a.c.) in high voltage overhead transmission lines. A change in the length of the MSM element causes voltage output in a linear variable differential transducer. The design of the NCIT was optimised for transmission lines. Several designs of its magnetic circuit were simulated using finite element package ANSYS APDL. Several key parameters were investigated to evaluate their effects on the sensitivity of the NCIT. Results are presented as the relationship between the current in the conductor and strain (linear elongation) of the MSM element. A commonly used conductor in high-voltage transmission lines was modelled together with the MSM element and the magnetic circuit. Recommendations have been made on the design of NCITs considering various parameters. In addition, analyses of errors in ANSYS models for the magnetic circuit have been presented. The developed methodology and obtained results are verified by comparing them to the results obtained through an experiment done by a manufacturer of MSM materials.

An underwater navigation system has specific requirements for reliability. In the frequently used strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integrated navigation system, the process of the DVL measuring velocity is always disturbed in a complex underwater environment. Thus the velocity measurements on acoustic beam directions are prone to get lost, which seriously affects system reliability. This paper proposes a novel information reconstruction and integration algorithm to solve this problem, based on the tree boosting model. The algorithm is mainly divided into two consecutive stages: training and reconstruction. When the DVL velocity in all beam directions is available, the GSCV-XGBoost (extreme gradient boosting based on the grid-search and cross-validation theory) method is used to train the mapping model between DVL velocity and SINS/DVL integrated navigation system velocity. When the DVL velocity is incompletely available, the mapping model, that has been trained in advance, is used to reconstruct the missing DVL velocity measurements and resume normal SINS/DVL integration conditions. Simulation and underwater vehicle-mounted experiments are applied to verify that the proposed algorithm reconstructs more accurate velocity information than traditional XGBoost, which shows great prospects for application and reliability in the SINS/DVL underwater integrated navigation system.

The intelligent rolling bearings fault diagnosis methods adopting a single vibration signal as the model input present low diagnostic precision, poor noise robustness, and difficulty in applying to variable operating conditions, so a multi-scale domain adaptation network (MSDAN) was put forward for variable load fault diagnosis of rolling bearings. This method combined multi-scale feature extraction with a lightweight convolutional neural network to extract complementary fault features from coarse-grained vibration signals at multiple time scales. Then, correlation alignment (CORAL) distance and domain identification adversarial learning were applied to extract domain invariant features to establish an end-to-end unsupervised fault diagnosis system for rolling bearings. The MSDAN model was evaluated using variable load-bearing datasets of two experimental setups and compared with other methods. The results show that MSDAN has better diagnostic accuracy and cross-domain adaptability than other domain adaptation fault diagnosis methods. In addition, our multi-scale method has more robust stability and generalization ability than any single-channel feature extraction method.

Global navigation satellite systems and inertial navigation systems (GNSSs/INSs) based on the cubature Kalman filter (CKF) can provide an effective land vehicle navigation and positioning solution. The accuracy of CKF depends mainly on the prior probability density function (PDF), which is inaccurate when the GNSS signal is temporarily blocked in an urban environment. Although the improved CKF (ICKF) can reduce cubature point errors by using the historical information obtained iteratively, the generation of cubature points is still related to measurement noise. To obtain a more accurate PDF, adjustment off the sensor measurement noise is necessary. In this paper, an adaptive ICKF for GNSS/INS system based on the Mahalanobis distance called MD-ICKF is proposed, which expands the usability of the micro-electronic mechanical system based integrated navigation system in an urban environment. Two lower triangular matrices obtained by Cholesky decomposition are used to adjust the cubature point errors during each iteration. Furthermore, the measurement noise will be inflated to obtain a more accurate PDF when it exceeds the threshold calculated by the MD and chi-square test. The field experiment results show that the proposed method has better positioning accuracy and performance than CKF and ICKF in an occluded environment

Machine learning is gradually applied to the fault diagnosis system of rotating machinery. However, the fault diagnosis system can only classify and identify the fault types previously trained by the model in the system. If the system is required to identify more types of faults, all the untrained new fault types and previously trained fault types need to be input into the model to retrain. Under the current background of big data, the upgrade time of fault types will be relatively long. To solve this problem, a parallel network model based on intrinsic component filtering (PICF) is proposed, in which each type of sample is trained separately, and then each type of training model is reduced in dimension, and finally the model we need is combined. The fault diagnosis framework based on the PICF is proposed. Firstly, the framework divides the input fault samples into training samples and test samples. Then the training samples are randomly segmented and input into the PICF training model, then the activation function is introduced to activate the training features and test features, and finally the softmax classifier is used for classification. The sparsity of order fault training in parallel network is discussed and the influence of sample segment number and nonlinear activation function on diagnosis is studied. Compared with other deep learning methods, the experiment results of the bearing and gearbox show that the proposed method can not only achieve higher fault classification accuracy under small sample training, but also update the model efficiently without reducing the diagnosis accuracy when increasing fault types.

Implementing condition monitoring and fault diagnosis of aero-engine bearings is crucial to ensure that aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings are extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability, and (2) they do not mine fault information sufficiently or efficiently. This article proposes a Siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearing fault diagnosis under small-sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired according to the matching rules to realize the expansion of the sample size. Second, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small-sample diagnostic methods. The experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.

Using deep learning to classify the time-frequency images of bearing vibration signals has become a mainstream method in the field of fault diagnosis. Most studies, however, assume a constant rotational speed, and the accuracy and reliability of the diagnosis model diminishes once the rotational speed changes. Moreover, due to the large model size and high computational complexity, the convolutional neural networks are not suitable for industrial applications. This paper proposes a novel fault diagnosis method for rotating machinery with variable speed based on multi-feature fusion and improved ShuffleNet V2. First, complementary ensemble empirical mode decomposition is used to denoise the time-domain signal. Then the denoised time domain signal is converted into an angular domain signal using the resampling technique, while the envelope spectrums of the angular domain signals are obtained by the Hilbert transform, and the three signals are fused into an red-green-blue image form to enhance the sample features. Finally, to perform fast and accurate classification of the features, the squeeze and excitation blocks are placed after the branch splicing operation of ShuffleNet V2 to enhance the recognition accuracy, and the rectified linear unit activation function is replaced by the HardSwish activation function to avoid necrosis. The experimental results show that the fault recognition accuracy of the proposed method for rolling bearings under variable speed is 96.4%, and the size of the fault diagnosis model is 7.82 MB, indicating that the method can effectively improve the accuracy and ensures that the model size does not increase significantly.

A method to improve the spatial resolution of the radiant optical fiber temperature sensor is proposed in this paper. The front lens of the sensor is designed to limit the diameter of the thermometry target. In order to avoid limiting the influence of the thermometry target diameter on the sensor radiation-light coupling ability, a mathematical model of the radiation-light coupling of the sensor is established, and the relationship between the radiation-light coupling ability of the sensor and the diameter of the thermometry target is analyzed. Furthermore, an aspheric lens is applied to reduce the influence of aberration on the diameter of the thermometry target. The experimental results show that the deviation between the actual temperature measuring target diameter and the theoretical design value is less than 0.2 mm. The radiation received by the sensor outside the temperature measuring target is less than 10% of its total received radiation. Moreover, when there is radiated light interference outside the temperature measurement target, the temperature measurement error of the sensor is only 0.21%–2.61%, indicating that the proposed method has practical significance in improving the spatial resolution of the temperature measurement.

The normalized microwave reflectance index (NMRI) based on global navigation satellite system (GNSS) interferometric reflectometry has been proven to reflect the changes in vegetation water content (VWC) effectively, but it is limited to point data. A spatially continuous NMRI product can be obtained by fusing NMRI data (point) and moderate-resolution imaging spectroradiometer (MODIS) data (surface). However, the time resolution of the existing results is limited to 16 d, and the research on the selection of modeling elements is not deep enough. In this paper, a point-surface fusion method for VWC retrieval considering the optimization of GNSS sites and elements is proposed. This method is aimed at using MODIS multi-band to synthesize vegetation indices with 8 d spatial-temporal resolution and establishing the initial element set by combining meteorological elements, followed by optimizing the GNSS sites and modeling elements for best modeling scheme, finally constructing the point-surface fusion method for VWC retrieval, and comprehensively evaluating the performance of the method. The results indicate that optimizing GNSS sites and modeling elements are particularly critical to improving modeling accuracy. Compared with other vegetation elements, normalized difference vegetation index (NDVI), gross primary productivity, and leaf area index are essential elements that affect the modeling effect. Among them, NDVI is the critical element. The NMRI products with 8 d/500 m resolution obtained by GA-BPNN can better reflect the change of VWC. Furthermore, the spatial performance of NMRI products is consistent with the fire forecast products and is suitable for drought and fire forecasts.

The vibration signals of rolling bearings are mixed with a variety of noises and a variety of faults which are coupled to each other, and which make the fault frequencies interfere with each other. This in turn leads to difficulty of accurate extraction of the features of composite faults. Therefore, it is necessary not only to suppress the noise of vibration signals but also to separate and extract the features of compound faults. A compound fault feature extraction method based on improved particle swarm optimization (IPSO) algorithm optimized maximum correlation kurtosis deconvolution (MCKD), improved variational mode decomposition (IVMD) and cyclic autocorrelation function (CAF), named IPSO-MCKD-IVMD-CAF is proposed. Firstly, the parameters of MCKD are selected adaptively by the IPSO, and the original signal is pre-processed by the parameterized MCKD. Secondly, the IVMD is utilized to decompose the signal, several intrinsic mode functions (IMFs) are obtained, the kurtosis and correlation coefficients of each IMF component are calculated. Finally, appropriate IMF components are selected by kurtosis and correlation coefficient to be superimposed into the reconstructed signals, and the reconstructed signals are analyzed by CAF. The proposed method has been successfully applied in simulation and measured vibration signal analysis, and the features of compound faults can be separated and extracted with high accuracy. The IPSO algorithm shortens the optimization time, and the IVMD solves the parameter selection problem. The analysis results of simulation case and measured data show the advantages of the proposed method IPSO-MCKD-IVMD-CAF. Compared to existing methods, the proposed method not only increases the accuracy of compound fault feature extraction, but also has a very good performance on the separation and extraction of compound fault features. More importantly, the improved algorithm reduces the computational complexity.

Based on the optimized exponential-degradation model (OEDM), a novel approach for predicting the remaining useful life(RUL) of roadheader bearings under different working conditions is proposed in this study. Specifically, the exponential process is used to construct the degradation process from a single performance characteristic under variable operating conditions, the generalized expectation maximization is employed to estimate model parameters, and the proposed degradation model is updated after new data is available. In the traditional exponential degradation method, the hyperparameters are only optimized, which leads to low calculation accuracy under severe working conditions. In the proposed method, the Bayesian algorithm and the Drift Brownian motion algorithm were respectively employed to optimize hyperparameters and stochastic parameters to ensure the high accuracy of the prediction results. In addition, degradation characteristics combined with sensory data acquired through condition monitoring were used to continuously update the RUL in the proposed degradation model. Finally, the effectiveness of the proposed model is verified by a simulation case and a case study. The results show that compared with the linear degradation model and the general exponential degradation model, the proposed OEDM performs well in practical applications and has a higher prediction accuracy. This study provides a reference for predictive maintenance of critical parts of tunneling machinery and cost reduction of tunneling.

The global navigation satellite system provides real-time and all-weather positioning with high accuracy. Under a good observational environment, short-baseline real-time kinematic (RTK) can provide centimeter-level positioning results. However, RTK without model correction of ionospheric delay can significantly reduce the positioning accuracy, and cannot achieve fast and high-precision positioning when the baseline is too long or heavily occluded. Therefore, we propose a combined RTK/fifth-generation (5G) mobile communication technology positioning model by combining global positioning system-RTK with 5G time-of-arrival observations to improve the positioning accuracy under medium and long baselines. Experimental validation and analysis were conducted based on the measured data of different baseline lengths. The results revealed that the combined RTK/5G positioning model markedly improved the positioning performance in both static and dynamic modes under medium- and long-distance baselines. In particular, the RTK/5G model can also achieve good positioning results in conditions where some satellites are occluded. The combined RTK/5G positioning model is important for achieving high-accuracy, real-time, and continuous positioning in complex environments.

Extensive fault information can be obtained from the vibration signals of rotating machines with faulty rolling bearings. However, the diagnosis of compound faults is challenging because of their easy mix-ups, which can lead to faulty diagnosis and judgment. This study improves the multichannel singular spectrum analysis (MSSA) by using convex optimization. In addition, an integrated fault diagnosis technology for rolling bearings using an improved MSSA and frequency–spatial domain decomposition was developed. This approach involves two primary stages: signal preprocessing and fault diagnosis. The proposed method was tested to diagnose faults in the rolling bearings of pellet mills. Signal preprocessing can significantly improve the quality of a vibration signal and preserve modal information that characterizes a fault. Fault diagnosis identifies the modal parameters entirely and accurately from the reconstructed vibration signal, and determines the degree of damage. The proposed method can aid in the robust diagnosis of faulty rolling bearings under severe operating conditions.

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

The generalized logarithm sparse regularization method (G-log) for fault diagnosis of rotating devices can effectively reconstruct repetitive transient shocks from noise-disturbed signals, but its reconstruction accuracy frequently becomes inferior due to unsuitable regularization parameters. Moreover, conventional sparse regularization methods perform nothing on the input signals to guarantee that the impulse characteristics remain constant during the entire iteration process, which exacerbates the influence of noise on the reconstruction accuracy. To overcome these challenges, an adaptive generalized logarithm sparse regularization method (AG-log) based on the second-order cyclostationary indicator (ICS2) and the improved maximum correlation Pearson correlation coefficient deconvolution (IMCPCCD) method is proposed in this paper. Firstly, the optimal threshold parameter k for each iteration of AG-log is determined based on the ICS2 criterion to ensure the optimal reconstruction accuracy, while the optimal combination of iteration numbers N and k is established. Secondly, the original signal and the IMCPCCD filtered signal are alternately used as the input signal of AG-log according to the parity of the iterative steps to reduce the interference of noise. Finally, the application on simulated and two engineering case signals demonstrates that AG-log has better reconstruction accuracy compared with conventional nonconvex sparse regularization methods.

In a wind turbine (WT) planetary gearbox system, the characteristics of incipient faults are difficult to recognize with increasing friction under a lack of lubrication condition. To this end, a translational–torsional coupled nonlinear tribodynamic model considering local faults is developed. A fitted time-varying transfer function is obtained using an impact experiment excited by a planet gear with a missing tooth and is further used to represent the modulation effect caused by the carrier. Using the proposed analytical model, the vibration response and modulation mechanism of the resultant signal under the coupling effect of tooth root crack and sliding friction are investigated. It is demonstrated that specific sidebands are greatly excited and the spectrum structure becomes extremely complex after the introduction of friction. Based on these results, sideband indicators are obtained to monitor the lubrication state and detect incipient faults. The experimental results based on an in-house WT drivetrain testbench are in good agreement with the simulation results.

This paper describes a large-scale visualization system that can simultaneously produce background-oriented schlieren (BOS) images and retroreflective shadowgrams (RS) of a compressible flow. The simultaneous measurements with BOS and RS in two optically equal systems allowed a direct comparison of the performance of the two methods and to quantify the relative differences, primarily with respect to identifying the broad range of length scales in a turbulent compressible flow. Further, direct comparisons are made between the schlieren image obtained from the BOS measurement, the displacement map obtained by subtracting a no-flow image from the unprocessed BOS record, the divergence of the BOS displacement field (the latter two are forms of a BOS-generated shadowgram), and the simultaneously obtained direct shadowgram from the RS setup. A qualitative comparison of the results indicated that all the three methods (BOS schlieren, direct difference, and divergence of the BOS-schlieren record) can capture the large-scale flow structures but that the smaller scales in the plume can only be resolved to a much lesser extent. These structures were, however, easily resolved in the RS images. The results show that, in the described system, the resolution of the BOS images is lower than that of the shadowgrams by a factor of about five. Further tests indicate that these differences in the spatial resolution of the BOS technique cannot be removed using a different design of the random-dot background pattern. These observations apply to the used large-scale system in which a minimum distance between the observed object and each of the two screens (background pattern/reflective) has to be maintained. This constraint also meant that neither system could be optimized with respect to spatial resolution.

We present a novel optical particle sensor technique using artificial neural networks. This method relies on observations of light scattering and extinction by particles as input features to a trained neural network, which provides relevant particle distribution and representative shape for an integrated particle mass flow estimation. The models are trained on artificial data, generated for particles that the sensor is likely to encounter. The feasibility of our method is demonstrated through an experimental measurement of solid sand particles injected into a high-speed wind tunnel. The results show accurate estimations of the injected sand mass flow and particle size statistics, with a sand mass flow root-mean-square error of 0.28 g min⁻¹ or 4.1$\%$ from the monitored rate using a precision scale. This measurement framework paves the way for sensor applications in harsh operating environments with limited optical access.

The full waveform decomposition technique is significant for LiDAR ranging. It is challenging to extract the parameters from non-Gaussian shaped waveforms accurately. Many parametric models (e.g. the Gaussian distribution, the lognormal distribution, the generalized normal distribution, the Burr distribution, and the skew-normal distribution) were proposed to fit sharply-peaked, heavy-tailed, and negative-tailed waveforms. However, these models can constrain the shape of the waveform components. In this article, the Gaussian convolution model is established. Firstly, a set of Gaussian functions is calculated to characterize the system waveform so that asymmetric and non-Gaussian system waveforms can be included. The convolution result of the system waveform and the target response is used as the model for fitting the overlapped echo. Then a combination method of the Richardson–Lucy deconvolution, layered iterative, and Gaussian convolution is introduced to estimate the initial parameters. The Levenberg–Marquardt algorithm is used for the optimization fitting. Through experiments on synthetic data and practical recorded coding LiDAR data, we compare the proposed method with two decomposition approaches (Gaussian decomposition and skew-normal decomposition). The experiment results revealed that the proposed method could precisely decompose the overlapped non-Gaussian heavy-tailed waveforms and provide the best ranging accuracy, component fitting accuracy, and anti-noise performance. However, the traditional Gaussian and skew-normal decomposition methods can not fit the components well, resulting in inaccurate range estimates.

The axial strain distribution of cast-in-place piles under the static load test is a reliable basis for analyzing the compressive bearing capacity of the pile foundation. However, it is still difficult to achieve high-precision, high-sensitivity, real-time, and distributed monitoring of the pile foundation at the same time. To improve the monitoring of the stress distributions of the pile foundation, a fixed-point ultra-weak fiber Bragg grating (UWFBG) strain-sensing optical cable is designed on the basis of the large capacity characteristic of UWFBG. The strain sensitivity of this optical cable is 1.15 pm μ⁻¹ within the range of 10 000 μ, which meets the accuracy requirements of pile health monitoring. The effectiveness of the designed UWFBG in pile foundation monitoring is verified through a static load test of the cast-in-place pile. The results show that the measured results of UWFBG and BOTDA (Brillouin optical time-domain analysis) have good consistency, and their average error is less than 7.5%. Compared with BOTDA, the UWFBG sensing system exhibits stronger anti-interference capability and faster response. The monitoring method proposed in this paper overcomes the shortcomings of previous monitoring methods in the static load test of the pile. The measured data can be used to calculate the detailed axial strain distribution of piles and analyze the distribution of axial force and side friction resistance of the pile. It not only provides a new monitoring method for static load test of cast-in-place piles, but also has great potential in monitoring large diameter pile.

In this paper, a noncontact load monitoring method based on laser-induced synchronous ultrasonic surface wave and air wave is presented to improve the accuracy of load measurement on pillar porcelain insulator. In order to eliminate the measurement error caused by insulator deformation, a correction algorithm for surface wave velocity calculation is established by introducing the air wave propagation time. An experimental setup of the load measurement system for pillar porcelain insulators based on laser-induced dual ultrasonic waves was assembled. A load calibration experiment and load detection experiment were carried out under various bending and torsional loads. The results showed that the proposed method is effective to solve the problem of the surface wave propagation time changing abruptly, greatly increasing the accuracy of load measurement. In the monitoring experiments, the relative error between the calculated load value and the actual applied load value was small, the average measurement error of the bending load was 16.20%, and the average measurement error of the torsional load was 11.38%. This proved that the measurement of the insulator load value using a laser ultrasonic surface wave is more precise than the traditional methods, making it more suitable for engineering inspection.

Temperature control in heat exchangers in reacting and non-reacting flows is of great importance for process optimization. In this context, phosphor thermometry is a promising technique for remote planar temperature sensing. The thermometry technique is based on exciting a luminescent material by a laser pulse and analyzing the subsequent phosphorescence signal. A particular interesting application is chemical hydrogen storage using liquid organic hydrogen carrier (LOHC) systems. The knowledge of temperature fields is of special interest for the characterization and understanding of hydrogen release from the carrier liquid. We investigated the luminescence properties of the thermographic phosphor (Sr,Ca)SiAIN₃:Eu²⁺ ('SCASN:Eu²⁺') dispersed in different heat transfer fluids, in particular LOHC systems, using a newly developed calibration cell. As heat transfer fluids may be excited to fluorescence by the laser as well, their absorption and florescence behavior is studied to develop an excitation and detection concept for thermometry. We found strong absorption of the heat transfer fluids from the UV range to a wavelength of about 400 nm. In addition, fluorescence signals were found in the visible wavelength range, which can interfere with the phosphor emissions. These fluorescence signals should therefore be circumvented by utilizing the different luminescence decay times in the chosen detection strategy. For thermometry, the SCASN:Eu²⁺ particles were excited by a laser sheet of a 532 nm Nd:YAG laser. A spectrometer and photomultiplier tube (PMT) were used to detect the emission spectrum and phosphorescence decay time (PDT). Two temperature evaluation strategies were applied, which are based on either the intensity ratio of two spectral emission regions (two-color laser-induced phosphorescence) or the PDT. The results obtained show an applicable measurement range between 293 K and 598 K for the intensity ratio method with a maximum relative sensitivity of 0.12% K⁻¹ at 293 K. For the PDT method, the phosphor allows measurements between 423 K and 598 K with a maximum relative sensitivity of 0.56% K⁻¹ at 598 K.

Noncoherent and coherent systems of linear frequency modulated continuous wave photonic radar under normal environmental conditions are constructed by co-simulation. In particular, the simulation models of different atmospheric humidity conditions are constructed to analyze the quantitative relationship between the system's ability to detect targets and humidity. It is discussed the relationship between detection distance and channel parameters under the premise of reasonable signal-to-noise ratio (SNR). In particular, the SNR of the noncoherent photonic radar is reduced to less than 10 dB when relative humidity >40%, which the laser power is 8 dBm and the detection distance is 100 m. However, the coherent photonic radar has a good performance of more than 21.7 dB. The results show that the noncoherent system is more susceptible to atmospheric humidity than the coherent system, and the heterodyne detection coherent system can achieve longer detection distance and better weak echo signal detection capability.

Real-time viscosity measurement techniques have been used to analyze the transition of hydrogels from a liquid state to a gel state. As viscosity is inversely proportional to diffusion coefficient, measuring real-time changes in viscosity can be done through passive rheometry with the addition of tracer particles. Particle diffusometry (PD) quantifies Brownian motion of sub-micron sized fluorescent particles by computing diffusion coefficients via statistical averaging. Herein, we demonstrate a method to study changes in diffusion coefficient as a function of time using PD for a temporally and spatially resolved rheometry measurement technique. We refined the PD algorithm using synthetic images of particles suspended in a liquid undergoing a sigmoidally decreasing diffusion trend to simulate the viscosity change of the solution during gelation. Then, the technique is applied to visualize the temporal and spatial gradients of diffusion coefficient during polyacrylamide hydrogel formation experiments. This work establishes the groundwork for quantifying over time changes in Brownian motion.

Volumetric Lagrangian measurements of droplet or turbulent flow using particle tracking methods have attracted intensive attention recently. The performance of three-dimensional particle tracking velocimetry (3D PTV) is highly reliant on the algorithms. Most existing 3D PTV algorithms are developed for multi-view systems, which cannot be applied directly to two-view systems due to the insufficient geometry constraints. In the current study, three different 3D PTV algorithms applicable for two-view systems are investigated parametrically using synthetic data. The imaging model is established on a two-view collimated shadowgraph imaging setup, which features a high framing rate, large test volume and long depth focus. The performances of the three algorithms are tested under different image particle densities and displacement–spacing ratios. The correctness of 3D reconstruction and tracking, as well as the number of ghost particles, are obtained and compared comprehensively. The results indicate that significant improvement is achieved through the dedicated designed algorithms. The comparative study reveals the potential of each algorithm with extremely limited geometry constraints in two-view systems, which may serve as guidance for choosing appropriate algorithms under different test conditions.

Bolt looseness detection is critical in preventing bolt connection failure. Compared to traditional sensor-based bolt looseness detection, image-based methods are low-cost and contactless and have thus become the highlight of research. However, current monocular vision-based detection methods are prone to error scaused by the camera perspective . In this paper, we present a novel bolt loosening angle detection method based on binocular vision. Key points on the bolt are detected and matched by SuperPoint Gauss network for 3D coordinates reconstruction and motion tracking. The bolt loosening angle is solved by fitting the rotation equation using random sample consensus. Experiments verify the proposed method performs well under different perspectives of camera and illumination conditions with an average error of 1.5°. Comparative test shows our method is superior to the monocular vision-based method in terms of accuracy when there is a large perspective angle. The proposed method is mark-free and robust to various working conditions, which makes it of great value for engineering application.

Under the action of water erosion and self-aging, reservoir dams are prone to develop cracks, which affect safe operation. Underwater visual imaging can be used to detect dam surface cracks, but spalling, aquatic plants and suspended sediments result in low image contrast and complex backgrounds. With the use of unsupervised machine learning, this paper proposes a fine segmentation and extraction algorithm for image-based dam surface cracks. First, adaptive histogram equalization is used to change the uneven illumination areas of underwater surface images intoeven illumination areas, whose statistical characteristics are calculated under linear spatial filtering. Second, the extraction problem of crack areas of interest after dodging preprocessing is transformed into calculating the distance of the image block cluster center, which can distinguish the image blocks of crack features from the background interference features. Third, the fine extraction of crack images is carried out by considering the connected domains and morphological features, and the posterior probability of an image sample category is obtained based on the soft clustering of a Gaussian mixed model. Finally, different extraction algorithms related to surface cracks are evaluated in extensive experiments. The results validate the superior performance of the proposed extraction algorithm with 90.1% extraction accuracy, 6.5% missing alarm rate and 7.2% false alarm rate.

A multi-scale optical flow estimation for the image captured by artificial compound eye (ACE) is investigated in this article. The optical flow estimation of ACE must be adapted by designing algorithms according to its unique multi-aperture characteristics. A more general filter for the regularization term, rather than a single iterative solution in the traditional variational model, is devised using the non-subsampled contourlet transform to enforce band decomposition and estimate the optical flow field. To circumvent the spillover and error of the single-aperture fringe flow field, a flow gradient weight is introduced to suppress it and enhance motion details. Furthermore, low-pass subbands adopt the Bayes threshold with the advantage of efficiently eliminating outliers. More high-pass subbands adopt guided filter with the benefit of separating important details from outliers. The prominent feature of the proposed method is that the accuracy of optical flow estimation is improved effectively by eliminating outliers. Finally, experimental results demonstrate the superiority of the examined optical flow estimation.

Close-range photogrammetry can be used to reconstruct dense point clouds of an object with very high surface coverage, making it useful for manufacturing metrology tasks such as part inspection and validation. However, compared to competing techniques, data processing times can be slow. In this paper we present a method to autonomously remove the background from the images within a photogrammetric dataset. We show that using masked images directly in the reconstruction results in much lower data processing times, with lower memory utilisation. Furthermore, we show that the point density on the object surface is increased while the number of superfluous background points is reduced. Finally, a set of reconstruction results are compared to a set of tactile coordinate measurements. Reconstructions with the background removed are shown to have a standard deviation in the point to mesh distance of up to 30 µm lower than if the background is not removed. This improvement in standard deviation is likely due to the static background, relative to the object on the rotation stage, causing triangulation errors when points are detected and matched on this background data. The proposed approach is shown to be robust over several example artefacts and can, therefore, be implemented to improve the measurement efficiency and measurement results of photogrammetry coordinate measurement systems.

The detection of strip steel surface defects is critical to ensuring the quality of strip steel products. Many deep learning-based methods have been presented and can achieve outstanding performance. However, most of these methods ignore the frequency information among defect areas, which plays an important role in defect detection. This paper proposes a deep learning method to further improve defect segmentation effects based on existing methods, called low-pass U-Net. Since most defects in strip steel are located in high-frequency areas, we implement a low-pass filter before downsampling in the encoder, which prevents aliasing and separates out high-frequency information. The high-frequency feature is transferred into the decoder to assist segmentation. Following previous studies, we propose an adaptive variance Gaussian low-pass layer to generate different filters according to each spatial location of the feature map, with lower computing resource use. Furthermore, to detect defects at significantly different scales, an improved Hypercolumn module is adopted at the end of the decoder to upsample and fuse the feature maps in different resolutions, where Subpixel replaces the bilinear interpolation to refine the upsampled results. The proposed method is validated on practical datasets and achieves considerable performance improvement (with a best Dice coefficient of 0.903), which demonstrates the effectiveness of low-pass U-Net. The introduction of the adaptive variance Gaussian low-pass filter layer results in a 3% increase in Dice coefficient in a comparative inference time, which achieves a balance in performance, inference time and complexity.

To improve the foreground segmentation and location accuracy of complex coal gangue images with gray histogram distribution close to the unimodal shape, a contour detection algorithm of the grayscale fluctuation matrix is proposed. The contour and non-contour pixels of coal and gangue images are investigated, and the result indicates that the gray values of the pixels around the contour exhibit the non-uniform distribution, and the gray value changes in different directions are significantly different. Accordingly, a grayscale fluctuation matrix is built by calculating the change amplitude of pixels in different directions, and multiple features are extracted from the grayscale fluctuation matrix to realize the target contour segmentation. Furthermore, the contour is optimized using the historical and future information of the contour image, thus effectively removing numerous false contours, reproducing some hidden contours and increasing segmentation accuracy. This method has high accuracy, and the maximum error rates of the pixel area and center coordinate of contour detection are 4.404% and 3.18% respectively. This study provides a feasible solution to the edge detection and segmentation of images with similar and complex backgrounds.

Motivated by recent research on two-phase flow void fraction measurement, this paper presents a study on void fraction measurements of a lubricant air-oil two-phase flow using a near infrared (NIR) optical-fiber spectrometer associated with partial least squares (PLS) regression models. To overcome the measurement inaccuracy due to interface scattering, this study used PLS regression models to analyse the spectrum of air-oil two-phase flow. First, an NIR optical-fiber spectrometer experimental system was developed. Second, a flow regime analysis was conducted for the bubble flow transition inside the experimental system. Finally, the PLS regression model validations, the void fraction measurement accuracies, and the uncertainties of the experimental system were evaluated and discussed, and the different PLS regression models were compared in detail. The results indicate that the NIR optical-fiber spectrometer combined with the PLS regression model can achieve void fraction measurements of lubricants in air-oil two-phase flow, with a maximum squared correlation coefficient (R²) of 0.981, a minimum Type A uncertainty of 0.039% and a minimum expanded uncertainty of 0.077%.

Spalling in rolling element bearings is a common localized defect generated during the operation of a bearing due to rolling fatigue. Size estimation of such localized defects can be helpful in determining the severity of the fault. This can be further used as an input for predicting the remaining useful life of the bearing. The popular approach for estimating the size (in terms of width) of a spall is to trace the entry and exit events of the rolling element while interacting with the fault. The time estimated between entry and exit of a rolling element from a pit-like spall can be converted to a geometric estimation of the fault size from the vibration signature. The present approach demonstrates the use of singular spectrum analysis (SSA) to accomplish this task. The vibration signal generated from the interaction of the rolling element with the localized fault is a hybrid signal consisting of a low frequency stepped response generated while the rolling element enters the fault superimposed onto the high frequency impact generated during the re-entry of the rolling element into the raceway from the spall. The signal information is enhanced via pre-processing with total variation regularization (TVR) filtration. The informative signal, which is extracted from the row temporal signal via SSA, aids in the accurate identification of entry and exit events. The proposed method integrating TVR with SSA for fault size estimation is validated using simulated signals and experimental signals from independent resources. The results show strong agreement with the accuracy level of size estimation.

The assessment method of anisotropic transmembrane transport coefficient vector P of a cell-spheroid under inhomogeneous ion concentration fields has been proposed by combining electrical impedance tomography (EIT) with an ion transport model to evaluate the anisotropic transmembrane transport of ions. An element P_i of P represents the transmembrane transport coefficient of the ith part of the cell membrane, which is assessed by the ion transport model from the average conductivity _i of the ith extracellular sector reconstructed by EIT. Anisotropic factor H obtained from P_i is introduced, which represents the anisotropic transmembrane transport. To validate our methodology, the inhomogeneous ion concentration fields are generated by injecting two tonicity-different sucrose solutions (isotonic, hypotonic or hypertonic) from both sides of the cell-spheroid. As a result, the inhomogeneous ion concentration distribution due to the anisotropic transmembrane transport is successfully observed from the reconstructed image by EIT. The anisotropic factor H shows that H = 0.34 ± 0.24 in isotonic and hypertonic combination, H = 0.58 ± 0.15 in isotonic and hypotonic combination and H = 0.23 ± 0.06 in hypertonic and hypotonic combination, respectively. To verify the results obtained by our methodology, the fluorescence ratio F [-] of potassium ions around the cell-spheroid is observed under three combinations as same as the EIT measurement. F shows the anisotropic transmembrane transport with the same trend with the EIT results.

Monitoring spatiotemporal variations of ionospheric vertical total electron content (VTEC) is crucial for space weather and satellite positioning. In the present study, an enhanced neural network (ENN) model is proposed to capture the changing characteristics of ionospheric VTEC and compared with the traditional mathematical models, i.e. the POLYnomial (POLY) model, generalized trigonometric series function and spherical harmonic function (SHF) model. The ionospheric VTEC data obtained from 31 permanent global positioning system stations in the southwest region of China on 26 August and 8 September, 2017, were used to test the performance of the mentioned models under different Solar-geomagnetic conditions. The ENN model is derived from the ensemble learning method, and the disadvantage that simple backpropagation neural network learners that are not robust enough is weakened by the ENN model. After statistical analysis and single-frequency precise point positioning (SF-PPP) experiments, it is demonstrated that the ENN model is superior to the above three mathematical models, regardless of the solar-geomagnetic conditions. In terms of mean absolute error, root mean square error, standard deviation, and mean absolute percentage error, the ENN model outperforms the SHF model, which is the best mathematical model in the analysis, by 40.7%, 30.20%, 29.88%, 38.04% under quiet solar-geomagnetic conditions, and by 37.66%, 29.93%, 30.96%, 32.01% under active solar-geomagnetic conditions. In addition, the accuracy of the SF-PPP is greatly affected by the error caused by ionosphere. In the static SF-PPP experiment of this study, the ENN model can better correct ionospheric error. Under quiet and active solar-geomagnetic conditions, the SF-PPP accuracy can be improved by 85.1% and 85.2% with the ionosphere delay correction from the ENN model.

Relative permeability is essential for understanding porous media's gas and water seepage characteristics and establishing production schedules in practical engineering applications. However, the movable water is too small to be detected in ultra-lowpermeability rocks, and it is difficult to determine the water saturation in the relative permeability measurement accurately. In this study, a differential pressure transducer (DPT) was applied to self-developed apparatus to quantify displaced water precisely. The results indicate that: (a) both the permeability and the relative permeability measurement results show high stability in repeatability tests with the application of DPT. (b) The final cumulative water flow data measured by the DPT is reliable; the relative error of the electronic balance and DPT value was less than 4%. (c) This self-developed instrument can obtain the relative permeability curve for ultra-low permeability rocks, such as tight sandstone and anthracite coal. Although there are limitations, this technique provides an economical and reliable pathway for studying the seepage characteristics of gas and water in ultra-low permeability rocks.

Auto-encoder (AE)-based condition monitoring (CM) methods for fault detection of wind turbines have received considerable attention due to their powerful feature extraction ability. However, traditional AE-based monitoring methods can only learn point-to-point features by minimizing reconstruction errors, which leads to a low sensitivity to anomaly data and weak robustness to noise data. To this end, we introduce a novel deep generative method based on the convolutional neural network (CNN)-conditional variational auto-encoder (CVAE). The key idea of CNN-CVAE is to unify the representation learning capacity of the CVAE and CNN. Specifically, CVAE can learn a probability distribution model by being trained on an anomaly-free supervisory control and data acquisition systems (SCADA) dataset; CNN and deconvolution operations are adopted for better time-series feature extraction and reconstruction performance. A statistical process control chart is applied to determine the alarm threshold. The effectiveness of the CNN-CVAE-based method is validated by datasets collected by SCADA installed in a commercial wind farm in China for impending blade breakage and gearbox failure. Abundant experiments with state-of-the-art deep learning-based CM methods are conducted, which indicate that our proposed method outperforms other methods in robustness, fault detection data sensitivity, fault warning time, and model parameters.

For the improvement of the safe operation of specific structures, it is crucial to monitor the bolt pretightening force. Conventional approaches frequently call for complicated systems that are challenging to install and cumbersome to operate in order to produce precise measurements. It is suggested that a passive wireless smart washer be used to track bolt pretightening force. The smart washer is installed together with the bolts, and a handheld terminal can be used at any moment to detect the tightening force of many bolts. Pretightening force and strain sensing are obtained by the washer structure, and the circuit in the washer obtains energy via a printed antenna to start the acquisition of a signal based on Near Field Communication technology. The data is then sent to the terminal for visualization and analysis. The prototype's experimental findings demonstrate that the smart washer can keep track of the bolt's pretightening force both during and after tightening.

In view of the difficulty in identifying the state of the micro turbine blade, this paper uses the order spectral entropy analysis method to extract the characteristic information of the blade fault based on the measured micro turbine bearing vibration data. Firstly, a micro-turbine test bench was established. The normal temperature stable inlet flow was produced by a blower, the high temperature and pressure unstable airflow was generated by turbojet combustion chamber, and the working inlet mode of a micro-turbine with a wide range of speed changes was simulated. The vibration signal of the bearing was collected by shell drilling, a variety of time-frequency domain feature analysis methods based on vibration signal are difficult to effectively identify blade faults under the combined action of unstable airflow and frequent variable speed. In this paper, the bearing vibration data in time domain is converted to the vibration data in angle domain, and then the order amplitude and entropy were compared and analyzed. The results show that the proposed method can effectively identify the blade fracture and fouling faults under the driving of stable and unstable airflow in the speed range of 0–20 000 r min⁻¹. This method provides a new method for micro turbine blade condition monitoring through bearing vibration data.

Oscillatory tribometry is presented as a new method for investigations on the transition from static friction to kinetic friction. The method distinguishes between purely elastic deformations and plastic frictional motions. Friction moduli are defined which are directly proportional to the stored and dissipated energy during an oscillation cycle. The elastic deformations are represented through the elastic friction modulus, which is directly proportional to the average elastic energy over an oscillation cycle, while the frictional motions yield the dissipation friction modulus, which is directly proportional to the average dissipated energy over an oscillation cycle. Energy dissipation in the static and boundary regime can be attributed to local dissolution of contact bonds or intermolecular adhesion bonds at the contact points, and thus to local slip. The ability to measure at very small sliding distances in oscillatory tribometer tests is a crucial prerequisite for such investigations. The methodology provides access to local friction phenomena at the intermolecular level while simultaneously measuring macroscopic samples. The application examples presented show that oscillatory tribometry on a highly sensitive rheometer is particularly valuable for applications such as dry contacts between polymers, elastomers or soft materials.

Currently, intelligent fault diagnostics of rotating machinery have significantly contributed to mechanical health monitoring. However, real-world labeled data obtained from high-value equipment such as gas turbine units, pumps, and other rotating components are occasionally insufficient for model training. This article proposes an unsupervised deep transfer learning model that can directly extract features from the data itself, thus reducing the number of training samples required. The well-designed neural network with a domain-specific antagonism mechanism aligns features between the source and target domains and so makes data-driven decisions more efficiently. The parameter-free gradient reversal layer is used as an optimizer, considerably reducing the cross-domain discrepancy and accelerating convergence. The average multi-classification accuracy under transferable conditions reaches 97%, 91%, and 95% over three cases of fault diagnosis. Moreover, the time consumption of the system improves by more than 3.5% compared to existing models. The results reveal that the suggested strategy is suitable for a challenging unlabeled dataset and represents a significant improvement over existing unsupervised learning techniques.

In the field of direct sensor-to-microcontroller interface circuits, a novel proposal for four-wire connected resistive sensors is suggested and experimentally tested herein. The circuit relies on measuring six discharging times corresponding to six RC circuits, which include the sensor resistance and the parasitic resistance of the wires. The circuit has been applied to measure resistors emulating a Pt100 thermal sensor with different values of wire resistance. Experimental results show that the accuracy and linearity of the proposed circuit is not deteriorated while increasing the wire resistance, either by the mismatch between the wire resistances. The error, with respect to the case with null wire resistances, is lower than 45 mΩ, while the non-linearity error is lower than 0.05% full-scale span in the worst scenario under test.