A review on convolutional neural network in rolling bearing fault diagnosis

作     者：Li, Xin Ma, Zengqiang Yuan, Zonghao Mu, Tianming Du, Guoxin Liang, Yan Liu, Jingwen 

作者机构：Shijiazhuang Tiedao Univ Hebei Prov Collaborat Innovat Ctr Transportat Powe Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Traff & Transportat Shijiazhuang 050043 Peoples R China State Grid Shijiazhuang Elect Power Supply Co Shijiazhuang 050051 Peoples R China 

出 版 物：《MEASUREMENT SCIENCE AND TECHNOLOGY》 (测量科学与技术)

年 卷 期：2024年第35卷第7期

页      面：072002-072002页

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 Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China [E2021210105] Natural Science Foundation of Hebei Province 

主　　题：rolling bearing fault diagnosis convolutional neural network hyperparameter optimization network structure optimization transfer learning interpretability 

摘      要：The health condition of rolling bearings has a direct impact on the safe operation of rotating machinery. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.