Rotating machinery fault detection and diagnosis based on deep domain adaptation:A survey

作     者：Siyu ZHANG Lei SU Jiefei GU Ke LI Lang ZHOU Michael PECHT Siyu ZHANG;Lei SU;Jiefei GU;Ke Li;Lang ZHOU;Michael PECHT

作者机构：Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and TechnologySchool of Mechanical EngineeringJiangnan UniversityWuxi 214122China HUST-Wuxi Research InstituteWuxi 214071China Center for Advanced Life Cycle EngineeringUniversity of MarylandCollege ParkMD 20742USA 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2023年第36卷第1期

页      面：45-74页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 080201[工学-机械制造及其自动化] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.52175096,51775243,11902124),the fellowship of China Postdoctoral Science Foundation(Grant No.2021T140279) 111 Project(Grant No.B18027). 

主　　题：Deep learning Domain adaptation Fault detection and diagnosis Transfer learning 

摘      要：In practical mechanical fault detection and diagnosis,it is difficult and expensive to collect enough large-scale supervised data to train deep networks.Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task,which performs well on small data and reduces the demand for high computation power.However,the detection performance is significantly reduced by the direct transfer due to the domain difference.Domain adaptation(DA)can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data.In this survey,we review various current DA strategies combined with deep learning(DL)and analyze the principles,advantages,and disadvantages of each method.We also summarize the application of DA combined with DL in the field of fault diagnosis.This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.