基于MHHO-BP算法的DC-DC电路软故障诊断  

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

作　　者：王力 李振壁[1,2] 姜媛媛 Wang Li;Li Zhenbi;Jiang Yuanyuan(College of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China;Department of Electronic and Information Engineering,Bozhou University,Bozhou 236800,China;Institute of Environmentally Friendly Materials and Occupational Health(Wuhu),Anhui University of Science and Technology,Wuhu 241003,China)

机构地区：[1]安徽理工大学电气与信息工程学院,淮南232001 [2]亳州学院电子与信息工程系,亳州236800 [3]安徽理工大学环境友好材料与职业健康研究院(芜湖),芜湖241003

出　　处：《电子测量技术》2022年第18期25-31,共7页Electronic Measurement Technology

基　　金：安徽省重点研究与开发计划(202104g01020012);安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助

摘　　要：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。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.

关 键 词：BP神经网络 故障诊断 变分模态分解 哈里斯鹰优化算法 DC-DC电路 

分 类 号：U226.81[交通运输工程—道路与铁道工程]