Domain adaptation-based deep feature learning method with a mixture of distance measures for bearing fault diagnosis

作     者：Zhou, Kaibo Cao, Guannan Zhang, Kaifeng Liu, Jie 

作者机构：Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China 

出 版 物：《MEASUREMENT SCIENCE AND TECHNOLOGY》 (Meas. Sci. Technol.)

年 卷 期：2021年第32卷第9期

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：National Key Research and Development Program of China [2020YFB1711203] Fundamental Research Funds for the Central Universities [2019kfyXJJS137] Key Laboratory of Ministry of Industry and Information Technology [KL2019W003] 

主　　题：rolling bearing domain adaptation fault diagnosis time-frequency image different working conditions 

摘      要：The success of deep learning-based bearing fault diagnosis depends on (a) training data and test data following the same data distribution;(b) a mass of labeled fault data being available. However, the working conditions of the bearings is changing, which leads to a difference in data distribution, and it is very laborious to obtain a mass of labeled data with fault information. To address these issues, a domain adaptation-based deep feature learning method with a mixture of distance measures for bearing fault diagnosis is proposed. First, the noise in vibration signal is filtered by wavelet packet decomposition and reconstruction. Then, frequency slice wavelet transform is used to transform the reconstructed signal into a two-dimensional time-frequency image. Furthermore, a domain-adaptative deep neural network based on ResNet50 is used for bearing fault diagnosis under different working conditions. A mixture of distance measures is used to minimize the distribution discrepancy between source and target domains. The bearing datasets provided by Case Western Reserve University and Paderborn University verify the effectiveness of the proposed method.