Advanced Fibre-Optic Sensing

Civil structures e.g. bridges, tunnels, and dams are essential to human societies. Currently, these complex engineered structures are challenged by aging issues. It is crucial to monitor the conditions of such structures in realtime to ensure their protection and conduct sufficient maintenance and rehabilitation when they begin to show omens of degradation or damage. Observation of Rayleigh scattering spectra from optical fibers using fiber Rayleigh reflectometry enables distributed sensing of static and dynamic strain in structural health monitoring for civil structures. Its key performance indices are the spatial resolution, the strain dynamic range, the measurement range, and the refresh rate. This article reviews tunable-wavelength optical time-domain reflectometry and coherent optical frequency-domain reflectometry and discusses the performance indices of each method in terms of the performance indices listed above. After analytical derivation, we have found that signal-to-noise ratios of both schemes are the same, which is a valuable discovery. In addition, we enumerate and review recent major industrial developments of both schemes.

Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing the performance of MMF sensors. Unlike conventional methods, machine learning techniques do not require complex structures or rare specialty fibers, which reduces fabrication difficulties and lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are thoroughly reviewed. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. The review provides a comprehensive discussion and outlook on MMF sensors, concluding that they are expected to be utilized in a wide range of applications.

This work addresses the historical development of techniques and methodologies oriented to the measurement of the internal diameter of transparent tubes since the original contributions of Anderson and Barr published in 1923 in the first issue of Measurement Science and Technology. The progresses on this field are summarized and highlighted the emergence and significance of the measurement approaches supported by the optical fiber.

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.

For the applications of distributed acoustic sensing technology (DAS), such as leakage detection in a pipeline, it is important to precisely identify the sensing channels along a optic fiber to correctly locate the vibrations and improve the accuracy of DAS. A method of precise sensing channel positioning for DAS is proposed. An experimental validation is conducted in consideration of optic fiber types and soil types. A bending element was inserted into the sand and clay to excite the constant-frequency signals point by point along the optic fiber. A DAS interrogator was used to collect the vibrations of the fiber. The average of the normalized time-domain signals and the amplitude of the specific constant frequency in the frequency domain are analyzed. The parameters of the skewness of the amplitude-channel curve (SACC) and the relative shift of the amplitude peak (RSAP) are proposed to identify the boundary of the adjacent sensing channels. The accuracy of the sensing channel positioning is better than the spacing of the excitation points.

A novel Mach–Zehnder interferometer (MZI) based on tapered-side-hole-fiber (SHF) fiber laser structure, which has obvious advantages in salinity and temperature measurement sensitivity, is analyzed both theoretically and experimentally. The tapered-SHF structure is used as a sensing element and an optical filter, which is connected to the erbium-doped fiber to form a fiber ring laser (FRL). Based on the linear correspondence between the output spectra of the FRL and the salinities, the salinity around the sensing area is measured by the MZI. The FRL has the unique advantages of high resolution, narrow line width, high optical signal-to-noise ratio, and high stability. Compared with the transmission spectrum of conical SHF in the broadband light source, these advantages are more suitable for sensing applications. The experimental results show that the finer the taper diameter, the higher the sensitivity of salinity and temperature. When the taper diameter is 8.23 μm, the salinity sensitivity of the sensor can reach 0.3347 nm/‰, and the temperature sensitivity can reach −0.4270 nm °C⁻¹.

This study fabricated an ultra-high refractive index (RI) sensor based on tapered no-core fiber (NCF) involving a simple inexpensive process. A splice section of NCF in the middle of single mode fiber was tapered to small diameters. The sensor was sensitive to the surrounding RI with a large measurement range of 1.3330–1.4437. The RI sensitivity differed with varying wavelengths, with a value of 41 916 nm/RIU at approximately 1550 nm, for the RI ranges of 1.4407–1.4437. It yielded a low temperature sensitivity of 8 pm °C⁻¹, which indicates an ultra-low temperature cross-sensitivity. The proposed fiber optic RI sensor can be used in many fields such as medicine and biochemical applications.

To address the problem of decreased recognition accuracy of event samples in practical phase-sensitive optical time-domain reflectometer (Φ-OTDR) monitoring scenarios due to external environmental interference, this paper proposes a feature correction algorithm based on sample feature weighting method. By establishing a correlation evaluation method and a weight allocation scheme based on sample feature correlation, combined with the back propagation (BP) algorithm, an average recognition rate of 99.50% for four types of events (climbing, strong wind, knocking and background, 6000 samples) in strong wind environments was achieved, which is 3% higher than the algorithm using BP classifier. The results demonstrate that the proposed algorithm can effectively enhance the performance of Φ-OTDR in complex environments.

Optical fiber Bragg sensors (FBGs) have great potential in the field of flexible wearable devices for tracking human gestures. Due to different human sizes, different wearable tensions inevitably cause errors when tracking arm joint movements. We have designed a flexible wearable smart sleeve with four heads of FBG and spandex polyurethane fibers (SPFs). SPFs sewn with flexible fabric sleeves convert elbow yawing and wrist pitching and roll into an axial strain of FBG. The measuring system has been developed to deduce personalized sensitivity using a dynamic calibration method. For males and females, dynamic calibration, verification and tracking tests were carried out. From the male's experimental data, the relative errors between the verification sensitivity and the personalized sensitivity are 1.93%, 5.85% and 7.16%, and the average relative errors between the tracking sensitivity and the personalized sensitivity are 7.09%, 5.58% and 2.52%, respectively. And from the data of the female's experiment, the relative errors between the verification sensitivity and the personalized sensitivity are 0.25%, 5.0% and 6.75%, and the average relative errors between the tracking sensitivity and the personalized sensitivity are 0.99%, 5.56% and 6.95%, respectively. The experimental data have shown that this wearable smart sleeve and the measuring system work well. The research results can be used to develop FBG sensing systems for monitoring joint movements for different human sizes on-line.

Probe-type micro-displacement sensors with a large range and high sensitivity have important applications in both aerospace and nano-lithography. However, the state-of-the-art measurement range achieved using conventional methods such as charge coupled device imaging and fiber grating demodulation is limited to only tens of micrometers. In this study, we propose and demonstrate a displacement sensing mechanism with a large range and high sensitivity for measuring linear displacements. The mechanism is based on a multimode encoding technique implemented on a surface nanoscale axial photonics (SNAP) microcavity platform. By tracking the transmittance variations of multiple axial modes and employing encoding techniques, we can determine the rough absolute position as well as the axial mode with the highest sensitivity in each region. Moreover, the selected mode for each region is exploited to accurately measure the micro-displacement with a large range and high accuracy. As a proof-of-principle experiment, the results indicate a large sensing range about 346 μm and a high sensitivity ranging up to 0.013 μm⁻¹. Assuming that the transmittance can be resolved by 0.1%, the resolution of the measurement is about 0.1 μm.

A novel hybrid interferometer sensor composed of a tapered seven-core fiber (TSCF) and a polydimethylsiloxane (PDMS) cap at the end face of a TSCF is proposed for simultaneous measurement of temperature and gas pressure. TSCF forms a Michelson interferometer (MI), and the PDMS cap on the end surface of TSCF forms a Fabry–Pérot interferometer (FPI). The sensing head consisted of a cascade of MI and FPI. When the external temperature or gas pressure changes, owing to the thermal effect or elastic deformation of PDMS, the interference spectrum of the FPI shifts significantly, so the FPI is very sensitive to temperature and gas pressure. MI, which is made of quartz optical fiber, is sensitive only to temperature and is not to gas pressure. The experimental results show that FPI has a temperature sensitivity of −0.22 nm °C⁻¹ in the temperature range of 40 °C–80 °C, and a gas pressure sensitivity of −2.27 nm MPa⁻¹ in the gas pressure range of 0–0.3 MPa. MI has a temperature sensitivity of 0.05 nm °C⁻¹ in the temperature range of 40 °C–80 °C, and a gas pressure sensitivity of zero in the gas pressure range of 0–0.3 MPa. Using the temperature and gas pressure sensitivities of FPI and MI to construct a measurement matrix, it is possible to simultaneously measure temperature and gas pressure, eliminating their cross-sensitivity. This sensor has the comprehensive advantages of compact structure, small size, easy manufacturing, low cost, high reliability, and high sensitivity, and is expected to be applied in industrial practice.

This work presents a study of a deflection laser sensor using a pump light source with different polarization states and shows that controlling the polarization state of the pump source can achieve better control in the tuning of an erbium-doped fiber laser. Laser tuning uses a selective wavelength filter manufactured using a thin core fiber section between two single-mode fibers, while the deflection is applied using an angular mechanism. In addition, the sensor was analyzed according to the wavelength shift of the laser emission as a function of the angular micrometric deflection, and a sensitivity of −33.01 pm µrad⁻¹ was obtained in a dynamic range from 0 to 89.3 µrad with an adjustment parameter R² = 0.993 61. We achieved dual-wavelength tuning with gradual shifting and single-wavelength tuning from 1531.5 nm to 1547.7 nm. This sensor exhibits potential applications in the bionic and robotic detection fields owing to its high sensitivity, good linearity, simple fabrication, and low cost.

The integration of artificial intelligence and distributed fiber optic acoustic sensing technology (DAS) has yielded remarkable results in recent years; however, some application scenarios face the challenge of acquiring an adequate amount of data for higher network accuracy. To address this issue, we propose a decoupling parallel convolutional neural network (DPCNN) that relies on multiple feature inputs to achieve higher accuracy while using smaller databases. Our model offers excellent recognition of five events, including background noise, footstep, digging, car passing, and climbing fence, with an accuracy rate of up to 94.9%. The DPCNN is a parallel and lightweight convolutional neural network (CNN) that boasts a short training time of only 3.76 s per epoch and a test time of 0.1175 s, with superior network convergence. In comparison to a mature single-branch CNN based on mixed images of time-frequency and time-space, the DPCNN accuracy is 6.4% higher. Our model demonstrates excellent performance across various databases and can achieve recognition accuracy of up to 98.7% with larger databases. Finally, we show the broad range of applications available for DPCNN based on multiple feature inputs when using a mature single-branch replacement in each branch of a two-branch network.

Currently, there is no automatic method available to measure the height of poured concrete during concreting of underground diaphragm walls (UDWs), and the conventional manual method interrupts the operation. To address this issue, a distributed fiber-optic sensing-based method for measuring the height of poured concrete during the concreting of UDWs was developed and successfully applied to the real-time measurement of the height of poured concrete. The proposed method was verified by performing a scale mode test, wherein an acrylic circular tube was used to simulate a UDW steel cage and a temperature-sensing fiber-optic cable was laid spirally to increase the spatial resolution of measurement to 0.1 m. The temperature distribution in the tube and real-time measurements of the height of the poured concrete during concreting were obtained. The proposed method was applied to the construction of a real UDW. A total of 3360 data points representing the spatiotemporal temperature distribution in the UDW steel cage were obtained. The results indicated that the temperature of the mud in the steel cage was approximately 25 °C–26 °C when no concreting operation was performed and that the temperature of the concrete layer increased to approximately 28 °C–31 °C during concreting. The height of the poured concrete was the boundary representing the transition from the temperature of the mud to that of the concrete, and the measurement precision reached ±0.5 m. The measurement results obtained via the proposed method were consistent with those obtained manually using a plumb bob, thus confirming the effectiveness of the proposed method for high-precision, real-time measurement of the height of poured concrete during the concreting of UDWs.

Intelligent perception of a scraper conveyor straightness and attitude monitoring of mechanical supporting equipment in the stope have practical and theoretical values for mining. This study proposed an optical fiber curvature sensor and a scraper conveyor's curve reconstruction method. The optical fiber curvature sensor comprises the fiber grating strain sensing optical cables, the flexible substrate, and the packaging material. The coordinate positions of each monitoring point are obtained through the strain–curvature conversion relationship and the slope recurrence algorithm, and then the reconstruction curve is obtained by fitting. The finite element simulation verifies the feasibility of the curve reconstruction method used for the deformation monitoring via optical fiber curvature sensors. The reconstruction error analysis results show that the root mean square error of reconstructions for two kinds of 2D plane bending and 3D space bending are 2.98%, 1.89%, and 3.13%, respectively. Their mean absolute errors are 8.9, 3.56, and 9.82 mm, respectively, verifying the feasibility and high accuracy of the proposed curve reconstruction equation. The research results provide a theoretical basis for the shape perception and straightening control of scraper conveyors in the intelligent working surface.

Fiber-optic distributed acoustic sensing (DAS) systems based on phase-sensitive optical time-domain reflection technology have been widely used for perimeter security and oil and gas pipeline safety monitoring. To address the problem of low recognition accuracy of high-sampling-rate long-sequence signal data (length greater than or equal to 1000 points) collected by the DAS system, we propose a CDIL-CBAM-BiLSTM network model based on feature fusion. The model uses a modified circular dilated convolutional neural network to extract detailed temporal structure information from each signal node, and combines it with bidirectional long short-term memory network using feature fusion to dig deeper into the data. Meanwhile, a convolutional block attention module was introduced to improve the model performance. The experimental results based on 5040 training samples and 2160 test samples show that the proposed model can achieve an average recognition accuracy of more than 99$\%$ for six real disturbance events under perimeter security scenarios, and the recognition time was less than 2 ms. In addition, our method achieved the highest recognition accuracy compared with other methods used in the experiments and can be extended to other areas, such as pipeline safety monitoring and industrial inspection measurements.

Optical fiber sensors based on the Brillouin optical time domain analysis (BOTDA) have good accuracy of crack opening displacement (COD) measurements. In this paper, we propose a method for COD quantification based on the area under the Brillouin frequency peaks induced by a crack. The study adopted a three-dimensional (3D) finite element model (FEM) to simulate the strain distribution within a segment of an optical fiber. The simulation results revealed that an increase in COD was associated with an increase in the Brillouin frequency peak area. The peak strain increased by 93 μ when the COD increased from 30 μm to 110 μm. The numerical findings were proved experimentally by employing a BOTDA interrogator for distributed sensing of strains. Two cracks in a 15-m-long steel beam were detected with the smallest error of 13%. The COD was predicted from the areas under the crack-introduced strain peaks under varying loads of 97, 196, 294 and 392 N. The effect of different spatial resolutions (10, 20 and 50 cm) and intervals (1, 2.5 and 5 cm) on sensing performance was discussed. Compared to previous research, the 3D FEM not only accurately predicted the changes in distributed optical fibers with cracks but also simplified traditional theoretical analysis. For the first time, a method has been introduced to predict cracks by comparing the area under the Brillouin peaks. This approach not only enhanced linearity but also reduced errors. The proposed method can be easily implemented in engineering practice for multi-point crack sensing in civil infrastructure.

The frequency-shift demodulation is a primary demodulation method in phase-sensitive optical time domain reflectometry (Φ-OTDR) with intrinsic resistance to interference fading. So far, the least mean squares (LMS) estimation method has the optimal demodulation accuracy and robustness. However, it takes much processing time due to the step-by-step sliding operation. In this work, we propose a fast LMS estimation method based on cross-correlation calculation to accelerate the demodulation while maintaining accuracy. Experiments are performed along a 9 km sensing fiber with a 4 m spatial resolution. The performance of the fast LMS, LMS, and cross-correlation methods are compared by using the same parameters. Compared with the LMS method, the fast LMS achieves a 12-time improvement in processing speed while remaining the same demodulation accuracy. Although the proposed fast LMS method takes slightly more time than the cross-correlation method (1.6 times), it improves the demodulation accuracy ∼6 dB for the vibration signal and ∼2.1 dB for the overall demodulation accuracy.

In efforts to resolve the limitations of incomplete bridge measurements in accurately identifying structural parameters, this study introduces a novel approach for precise measurement of full-field bridge strain and displacement using a limited number of long-gauge fibre Bragg grating strain sensors. This method consists of two algorithms: a double reconstruction algorithm for strain reconstruction and an improved conjugate beam algorithm (ICBA) for displacement identification. The dual reconstruction algorithm exploits proper orthogonal decomposition to establish a comprehensive mapping relationship between units, thereby achieving precise strain estimation. This intricate mapping process enables the algorithm to accurately compute strain responses. By leveraging the derived full-field strain data, the ICBA effectively captures complete displacement responses. The conventional sensor placement configuration monitors only a limited number of units. To enhance full-field measurement accuracy, this method categorizes unmonitored units into two levels based on sensor placement. The double reconstruction algorithm then estimates the strain response sequentially, contributing to enhanced precision. Numerical simulations validate the proposed method (PM), which is demonstrated to be efficient and robust under various vehicle loads, impact loads, and noised levels. A physical experiment further demonstrates the efficacy of the PM in practice. The results underscore the potency of the PM as powerful theoretical and practical approach for full-field strain and displacement measurement.

A polarized low-coherence interferometer (PLCI) based on a liquid crystal (LC) wedge is designed, and an associated demodulation method encompassing the tunability feature is proposed for tunable, standalone optical sensing. The application of an electric field to the LC material effectively decreases the birefringence value and the related dispersion relation, which in turn enhances the resolution of detection. The effect of the electric field on the demodulation of the cavity length is addressed by the successive determinations of the centroid positions of the PLCI interferograms. Through a comprehensive study of numerical simulations, the effectiveness of the proposed approach is explored relative to the conventional envelope detection methodology. In order to verify this method, an experiment with a Fabry-Perot-based fiber optic displacement sensor is carried out using a 5CB LC wedge-based PLCI setup in the presence of an electric field. The measurement accuracy of the cavity length is found to be 0.74% of full scale, rendering it more precise and robust than the conventional envelope detection method.

The solidification sequence during the solidification of fusion-cast explosives is an important parameter for the optimization of the manufacturing process, which can be analyzed by using numerical simulation experiments. However, the numerical simulations are not totally reliable due to the inherent errors in the algorithms and parameters. To address this issue, a measurement method is proposed to monitor the solidification process based on the embedded method of distributed fiber optic sensing. And a method is developed to identify the solid–liquid phase change interface region, which can be effectively demodulated and analyzed for sensing data. The experimental results were verified by using numerical simulations based on casting simulation software and compared. It can be found that the total solidification time and pattern of both are relatively consistent. However, some of the solidification characteristics in the numerical simulation are lack of precision due to the inaccuracy of the heat transfer parameters.

In this paper, we propose a novel structure of a photonic two-dimensional force perceptron based on fiber Bragg gratings for the first time to demonstrate microforce detection and direction recognition in an all-optical transmission sensing platform. Silicone gel was used to encapsulate the Bragg gratings arranged in equilateral triangles with specific side lengths in a hollow cylindrical structure. The force stimulation is sensed by stress transfer ball located on top of the perceptron, and the force is transmitted to three Bragg gratings arranged in equilateral triangles through the encapsulation material. The perceptron can achieve force detection with a sensitivity of $0.06$${\text{mN}}$ and a force stimulation resolution of $0.1$${\text{mN}}$. The reset operation mainly relies on the high elastic modulus of the material itself. The results demonstrate continuous optical sensing of microforce stimulation ranging from $0$${\text{mN}}$ to $2$${\text{mN}}$, In addition, we presented the results of large-scale directional force stimulus recognition, successfully achieving directional recognition of force stimuli. Furthermore, we achieved angle recognition with an error rate of $1\%$, prove the practical potential of constructing a precise positioning continuous all-optical force sensing platform.

In addressing the significant security concerns associated with urban natural gas transportation, particularly the potential catastrophic consequences of leaks, this paper introduces a novel approach for detecting and locating leaks in natural gas pipelines using weak fiber Bragg grating (w-FBG) array micro-strain sensing technology. The field pipeline model is established, w-FBG array is applied to pipeline leakage experiment, and the pipeline leakage is detected by analyzing the change trend of micro-strain. The pipeline leakage experiments under different pressures were carried out. The experimental results show that there is a good linear relationship between the pipeline leakage pressure and the wavelength change of w-FBG, and the linearity is greater than 0.99, which is consistent with the theoretical analysis and verifies the applicability of w-FBG. According to the characteristics of micro-strain transfer in pipeline leakage process, a 'abrupt change point' capturing method based on peak to peak slope and threshold judgment is proposed. Results indicate that the proposed algorithm effectively detects pipeline leaks and accurately locates the point of leakage, with a positioning error of approximately 0.5 m. Compared with the traditional method, the proposed method has higher precision.

To address the challenges associated with sample injection into the air hole of photonic crystal fiber (PCF) and collimation, in this paper, we assemble a single-mode photonic crystal single-mode fiber structure sensor chip based on the Mach–Zehnder interference principle using microfluidic chip processing technology. The sensing principle is analyzed mathematically and the sensing characteristics are verified theoretically and experimentally. The temperature sensitivity of the sensor is −1.3325 nm °C⁻¹, and the refractive index sensitivity is 1666.2 nm RIU⁻¹. This structure solves the difficulty of filling and coupling PCFs. Furthermore, it introduces a novel research methodology for the design and assembly of high-performance biosensors based on PCFs.