Abstract
Rolling bearings based rotating machinery are widely used in various industrial applications. The failure of rolling bearings, as one of the most critical components, would lead to disastrous consequences to the machinery. Therefore, it’s paramount to deliver an effective intelligent fault diagnosis method for rolling bearings to ensure the machinery’s stability and reliability. With this aim, this article proposes a novel approach that features are extracted via an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the faults are identified based on a semi-supervised clustering algorithm, that is, clustering approach of fast search and discovery of density peaks (CFSFDP). The proposed method provides two main contributions: (1) highly representative important features may be derived from common high-dimensional features, and (2) the intelligent semi-supervised classifier can identify faults type adaptively without large amount of type-labelled data unlike other supervised classifiers. Benchmarking studies were carried out to indicate that the proposed methodology for the fault diagnostic is superior to other common-used approaches.
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The authors confirm that there is no conflict of interest in the manuscript.
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Jun Wu
Jun Wu received the B.S. degree from the Hubei University of Technology, Wuhan, China, in 2001, and the M.S. and Ph.D. degrees in mechanical engineering from the Huazhong University of Science and Technology (HUST), Wuhan, in 2004 and 2008, respectively. He is currently a Full Professor with the School of Naval Architecture and Ocean Engineering, HUST. He was a Visiting Scholar with the Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA, from 2014 to 2015, and 2019, where he conducted technical research in the area of structure health monitoring. He has more than 70 publications and holds 15 patents. His research interests include equipment health monitoring, fault diagnosis, and remaining useful life prediction.
Manxi Lin
Manxi Lin is now pursuing his master degree with the School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology (HUST), Wuhan. His research interests include equipment health monitoring, fault diagnosis, and remaining useful life prediction.
Yaqiong Lv
Yaqiong LV received the B.Eng. degree in industrial engineering from Huazhong University of Science and Technology, Wuhan, China, 2008. the Ph.D. degree in system engineering and engineering management, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 2013. She is currently an Associate Professor with school of Transportation and Logistics engineering, Wuhan University of Technology. Her research interests include system reliability/resilience analysis, big data analytics, and intelligent scheduling.
Yiwei Cheng
Yiwei Cheng received the B.S. degree in marine engineering from Dalian Maritime University, Dalian, China, in 2016; the Ph.D. degree with the School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China 2021. He is now an Associate Professor with School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan. His main research interests include big data analytics, health monitoring, intelligent fault diagnosis and remaining useful life prediction for equipment diagnosis, and remaining useful life prediction for equipment.