河北科技大学学报2025，Vol.46Issue(2)：129-140,12.DOI:10.7535/hbkd.2025yx02002
基于CWT-IDenseNet的滚动轴承故障诊断方法
Fault diagnosis method for rolling bearings based on CWT-IDenseNet
摘要
Abstract
Aiming at the problems of incomplete information contained in one-dimensional signals and overfitting of the DenseNet under variable working conditions,a rolling bearing fault diagnosis method based on continuous wavelet transform(CWT)time-frequency images and an improved densely connected convolutional network(IDenseNet)was proposed.Firstly,the one-dimensional vibration signal was converted into two-dimensional time-frequency images by CWT.Then,the DenseNet network was turned into IDenseNet,the ReLU activation function in the first convolutional block of DenseNet was replaced by the Swish activation function(which was smoother),and the style-based recalibration module(SRM)and the convolutional block attention module(CBAM)were introduced into the DenseNet network.The SRM focused on the weight of feature channels,while CBAM enhanced the feature expression ability from the two dimensions of channel and space.Finally,the two-dimensional time-frequency image was input into the IDenseNet model for feature extraction and fault diagnosis,and the fault diagnosis results were output through the Softmax layer of the model.The results show that the average fault recognition accuracy of the proposed method reaches 97.80%under constant and variable conditions,and the average fault recognition accuracy reaches 99.44%in the transfer learning model.The CWT-IDenseNet method can effectively improve the generalization ability of the model,which has significant advantages under constant and variable conditions,providing reference for improving the accuracy and reliability of rolling bearing fault diagnosis.关键词
机械动力学与振动/滚动轴承故障诊断/连续小波变换/密集连接卷积网络/注意力机制Key words
mechanical dynamics and vibration/fault diagnosis of rolling bearing/continuous wavelet transform/densely con-nected convolutional networks/attention mechanism分类
机械工程引用本文复制引用
贾广飞,梁汉文,杨金秋,武哲,韩雨欣..基于CWT-IDenseNet的滚动轴承故障诊断方法[J].河北科技大学学报,2025,46(2):129-140,12.基金项目
国家自然科学基金(52206224) （52206224）
中央引导地方科技发展资金项目(226Z1906G) （226Z1906G）
河北省教育厅科学研究项目(CXY2024038) （CXY2024038）
