基于MHHO-BP算法的DC-DC电路软故障诊断

针对DC-DC电路软故障诊断中特征提取困难和分类准确率低的问题，提出了一种基于多策略改进哈里斯优化算法-反向传播MHHO-BP)神经网络的故障诊断方法。该方法通过VMD对故障信号进行处理，提取其时域和频域特征作为故障向量，采用MHHO算法优化BP神经网络的权值和阈值，建立DC-DC电路的VMD-MHHO-BP软故障诊断模型。实验结果表明，对于DC-DC电路软故障，该方法相较于鲸鱼优化算法(WOA)和蝴蝶优化算法(BOA)优化BP神经网络，其诊断效果好，准确率高。

Soft fault diagnosis of DC-DC circuits based on MHHO-BP algorithm

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.