机械科学与技术2025，Vol.44Issue(12)：2090-2098,9.DOI:10.13433/j.cnki.1003-8728.20230365
一种用离散多小波变换的可解释卷积神经网络在滚动轴承故障诊断中的应用
Application of an Interpretable Convolutional Neural Network Using Discrete Multi-wavelet Transform to Fault Diagnosis of Rolling Bearing
摘要
Abstract
In view of the lack of interpretability and weak anti-noise ability of current intelligent diagnosis models in fault diagnosis of rotating machinery,an interpretable diagnosis model based on the discrete multi-wavelet transform and convolution layer fusion is proposed.Firstly,the discrete multiwavelet layer(DMWL)is constructed by using the discrete multiwavelet filter,and the input vibration signal is decomposed into multiple frequency component signals by using the multiwavelet filter,and then the information of each frequency band is learned with the one-dimensional convolution layer to realize the fault information location of the model.In order to enhance the ability of discrete multi-wavelet transform convolutional neural network(DMWT-CNN)model to learn the information of each frequency component,the frequency attention mechanism(FAM)was introduced.Finally,it is verified in two kinds of rolling bearing data sets and a variety of diagnostic models.The research results show that the model can locate and detect fault information,and the diagnostic accuracy reaches 90%under the interference of-6 dB Gaussian white noise,which is higher than other diagnostic models,and has interpretability and good robustness.关键词
故障诊断/离散多小波层/可解释性/鲁棒性Key words
fault diagnosis/discrete multiwavelet layer/interpretability/robustness分类
机械制造引用本文复制引用
HU Haibin,LIU Renxin,XIA Yuwen,LIU Rilong..一种用离散多小波变换的可解释卷积神经网络在滚动轴承故障诊断中的应用[J].机械科学与技术,2025,44(12):2090-2098,9.基金项目
江西省研究生创新专项(YC2023-S394) （YC2023-S394）
