湖北民族大学学报（自然科学版）2025，Vol.43Issue(2)：259-265,7.DOI:10.13501/j.cnki.42-1908/n.2025.06.004
基于GSABO-VMD-CNN-BiLSTM模型的电力电子电路软故障诊断
Soft Fault Diagnosis of Power Electronic Circuits Based on GSABO-VMD-CNN-BiLSTM Model
摘要
Abstract
To address the low diagnostic accuracy issue caused by insufficient signal features and noise in the soft fault diagnosis of traditional power electronic circuits,a DC/DC golden sine adaptive backpropagation optimization-variational mode decomposition-convolutional neural network-bidirectional long short-term memory(GSABO-VMD-CNN-BiLSTM)model was proposed for soft fault diagnosis of power electronic circuits.Firstly,the GSABO algorithm was applied to optimize the VMD parameters in order to solve the problems of mode aliasing and endpoint effects.Secondly,the minimum envelope entropy and minimum arrangement entropy were combined to construct a composite fitness function,and the wavelet threshold function was utilized for denoising to improve the data quality.Finally,the time-domain features were extracted and input into the CNN-BiLSTM model to complete the fault diagnosis.The model was experimentally verified by the 150 W Boost circuit,and the results showed that the accuracy of the model reached 99.58%.And under different signal-to-noise ratios,the model performed well in terms of accuracy,recall,and other indicators.The model could be effectively used for soft fault diagnosis of power electronic circuits.关键词
DC/DC电路/变分模态分解/小波阈值/复合适应度函数/CNN-BiLSTMKey words
DC/DC circuits/variational mode decomposition/wavelet threshold/composite fitness function/CNN-BiLSTM分类
交通工程引用本文复制引用
范皖北,姜媛媛..基于GSABO-VMD-CNN-BiLSTM模型的电力电子电路软故障诊断[J].湖北民族大学学报（自然科学版）,2025,43(2):259-265,7.基金项目
安徽省重点研究与开发计划项目(202104g01020012). （202104g01020012）
