Intelligent fault diagnosis of rolling bearings based on clustering algorithm of fast search and find of density peaks

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.

Keywords:

• CEEMDAN
• CFSFDP
• feature extraction
• intelligent fault diagnosis
• rolling bearing

Disclosure statement

The authors confirm that there is no conflict of interest in the manuscript.

Additional information

Funding

The work was supported in part by the National Natural Science Foundation of China (NSFC) under grant No. 51875225 & 72101194 and in part by the National Key Research and Development Program of China under the Grant No. 2018YFB1702302 and in part by the Key Research and Development Program of Guangdong Province under the Grant No. 2019B09091600 and in part by the Humanities and Social Science Foundation of Ministry of Education of China Grant No. 20YJC630096.

Notes on contributors

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.