Abstract
This article gives a scintillation detector fault diagnosis method based on BP neural network. From the aspect of output signals of scintillation detectors, the wavelet packet transform is used to extract the energy characteristic vectors which are treated as the input of BP neural network, and a training database is established as well as BP neural network parameters are optimized. Then the method is employed to establish a fault recognition model and fault types can be concluded. Finally, the simulation data are compared with those of two other methods (the statistical diagnosis method and an method based on multi-classification support vector machine). The experimental results illustrate that the application of proposed method can improve the fault diagnosis accuracy of scintillation detectors effectively.