现代电子技术2024，Vol.47Issue(12)：115-121,7.DOI:10.16652/j.issn.1004-373x.2024.12.020
基于优化VMD-CNN-BiLSTM的电机轴承智能故障诊断研究
Research on intelligent diagnosis of motor bearing faults based on optimized VMD-CNN-BiLSTM
摘要
Abstract
In allusion to the problems of low fault diagnosis accuracy and low fault diagnosis efficiency caused by weak early fault signal and poor feature data extraction effect of rolling bearing,a fault diagnosis method combining signal processing and deep neural network is proposed.The variational mode decomposition(VMD)is used to extract the feature data of main bearing vibration data.In order to determine the optimal number of modal components K and penalty parameters in the VMD algorithm α,and enhance the effectiveness of feature extraction,the minimum permutation entropy is used as the fitness function,and the sine chaos adaptive whale optimization algorithm(CAWOA)with strong global optimization ability is used to determine the parameters and obtain the optimal modal component.The feature vector is constructed based on the optimal modal components,which is used as inputs of the CNN(convolutional neural network)BiLSTM(bidirectional long short term memory)network to realize the fault classification.Based on the data collected from the experimental platform,the experimental analysis results show that in comparison with other fault diagnosis models,the optimized VMD-CNN-BiLSTM bearing fault diagnosis model can significantly improve accuracy and real-time performance.关键词
变分模态分解(VMD)/卷积神经网络(CNN)/双向长短期记忆(BiLSTM)/滚动轴承/智能故障诊断/特征数据提取/正弦混沌自适应鲸鱼优化算法Key words
variational mode decomposition/convolutional neural network/bidirectional long short term memory/rolling bearings/intelligent fault diagnosis/feature data extraction/sinusoidal chaos adaptive whole optimization algorithm分类
电子信息工程引用本文复制引用
曹景胜,于洋,王琦,董翼宁..基于优化VMD-CNN-BiLSTM的电机轴承智能故障诊断研究[J].现代电子技术,2024,47(12):115-121,7.基金项目
国家自然科学基金项目(51675257) （51675257）
国家自然科学基金青年基金项目(51305190) （51305190）
辽宁省教育厅基本科研项目(面上项目)(LJKMZ20220976) （面上项目）
辽宁省自然科学基金指导计划项目(20180550020) （20180550020）
