Abstract:To address the problems of difficult feature extraction and low classification accuracy in soft fault diagnosis of DC-DC circuits, a fault diagnosis method based on multi-strategy improved Harris optimization algorithm-back propagation (MHHO-BP) neural network is proposed. The method processes the fault signal by VMD, extracts its time-domain and frequency-domain features as the fault vector, and uses the MHHO algorithm to optimize the weights and thresholds of the BP neural network to establish a VMD-MHHO-BP soft fault diagnosis model of DC-DC circuits. The experimental results show that for soft faults of DC-DC circuits, the method has good diagnostic effect and high accuracy compared with the Whale Optimization Algorithm (WOA) and Butterfly Optimization Algorithm (BOA) optimized BP neural network.

Abstract:A circuit soft fault diagnosis method based on Improved Whale (IWOA) Optimized Support Vector Machine (SVM) is proposed to address the problem of DC-DC circuit soft fault diagnosis with low accuracy. First, a VMD is performed on the fault signal to extract the feature vectors. Then we improve the global search ability of the traditional whale algorithm to prevent falling into local optimum by referring to the feedback mechanism, and change the linear factor to a nonlinear factor to balance the global search and local exploitation ability to improve the whale algorithm to solve the problem of easily falling into local optimum and low local exploitation ability. Finally, the IWOA-SVM model is used for soft fault diagnosis of circuits, and finally, the problem of low accuracy of soft fault diagnosis of circuits is achieved with high efficiency. Based on the results of fault diagnosis, it is shown that the improved whale algorithm optimized support vector machine has better diagnosis effect compared with other methods compared in this paper. Fault recognition accuracy of 99.1667%.