Open AccessX-ray Power Fault Detection Method Based on Feature Spectrum Reconstruction and Convolutional Neural Network
Author(s) -
Jianlong Zhang,
Mengying Cui,
Bin Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012057
Subject(s) - convolutional neural network , fault (geology) , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , computer science , wavelet , support vector machine , spectral density , wavelet transform , fault detection and isolation , zigzag , power (physics) , feature vector , algorithm , mathematics , telecommunications , physics , linguistics , philosophy , geometry , quantum mechanics , seismology , actuator , geology
The high frequency of X-ray high-voltage power supply (XHPS) leads to conspicuous parasitic effect of power components. And this will transform the equipment into a time-varying and nonlinear complex system. By applying the combination of convolutional neural network (CNN) and traditional methods, this paper proposes a fault detection method based on 2-D feature spectrum reconstruction and CNN. Firstly, the multi-wavelet transform is utilized to decompose the 1-D high-voltage power signal to obtain the coefficients of each frequency band. Secondly, the inverse Zigzag scan reconstructs the multi-wavelet coefficients into a feature spectrum that satisfies the input form of VGG-16, and then cascades the deep features obtained by VGG-16 with the multi-wavelet features. Finally, the final fault detection result is obtained by the support vector machine (SVM). The simulation results show that the proposed method has better fault detection performance and could provide a workable idea for fault prediction and avoidance.