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.

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This paper presents a scintillation detector fault diagnosis method based on wavelet packet analysis and multi-classification support vector machine(multi-SVM). In the proposed method, a wavelet packet algorithm is used to analyze waveform characteristics of output signals caused by different faults of detectors, then characteristic vectors can be extracted. The multi-SVM is employed to establish a fault recognition model and fault types can be concluded. Performances of the proposed method are validated by experimental data obtained from the plastic scintillation detector for the single fault diagnosis and hybrid fault diagnosis. The experimental results show that faults can be diagnosed automatically and quickly by analyzing signal waveform features. K: Analysis and statistical methods; Models and simulations; Radiation monitoring; Gamma detectors (scintillators, CZT, HPGe, HgI etc) 1Corresponding author.

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