摘要
Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.
基金
supported by the National Natural Science Foundation of China(62020106003,61873122,62303217)
Aero Engine Corporation of China Industry-university-research Cooperation Project(HFZL2020CXY011)
the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(MCMS-I-0121G03).
作者简介
LEI Xue was born in 1991.She received her M.S.degree in control theory and control engineering from Lanzhou University of Technology,Lanzhou,China,in 2018.She is currently pursuing her Ph.D.degree in control theory and control engineering with the College of Automation Engineering,Nanjing University of Aeronautics and Astronautics.Her current research interests include data-driven fault diagnosis,fault prognosis,and rotating machinery fault and health management.E-mail:leixue@nuaa.edu.cn;Corresponding author:LU Ningyun was born in 1978.She received her Ph.D.degree from Northeastern University,Shenyang,China,in 2004.From 2002 to 2005,she worked as a research associate and post-doctoral fellow in Hong Kong University of Science and Technology.She is currently a full professor at the College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,China.Her research interests include data-driven fault prognosis and diagnosis and their applications to various industrial processes.E-mail:luningyun@nuaa.edu.cn;CHEN Chuang was born in 1992.He received his M.Eng.degree in control engineering from Nanjing Tech University,Nanjing,China,in 2018,and Ph.D.degree in control theory and control engineering from Nanjing University of Aeronautics and Astronautics,Nanjing,China,in 2022.From March 2021 to April 2022,he became a China Scholarship Council(CSC)-funded joint Ph.D.student at York University,Canada.He is currently a lecturer in College of Electrical Engineering and Control Science at Nanjing Tech University,Nanjing,China.His current research interests include datadriven fault prognosis and health management.E-mail:chenchuang@nuaa.edu.cn;HU Tianzhen was born in 1996.She received her M.S.degree from Nanjing University of Aeronautics and Astronautics,Nanjing,China,in 2020.She is currently pursuing her Ph.D.degree in control science and engineering at Nanjing University of Aeronautics and Astronautics.Her research interests include data-driven fault diagnosis and fault propagation analysis.E-mail:hutianzhen@nuaa.edu.cn;JIANG Bin was born in 1966.He received his Ph.D.degree in automatic control from Northeastern University,Shenyang,China,in 1995.He had ever been a post-doctoral fellow,a research fellow,and a visiting professor in Singapore,France,USA and Canada,respectively.He is currently a chair professor of Cheung Kong Scholar Program in Ministry of Education,and the vice president of Nanjing University of Aeronautics and Astronautics,Nanjing,China.His current research interests include fault diagnosis and fault tolerant control and their applications in aircrafts,satellites and high-speed trains.E-mail:binjiang@nuaa.edu.cn。
