Abstract
The emergence of prognostics and health management as a condition-based maintenance approach has greatly improved productivity, maintainability, and most essentially, reliability of systems. Invariably, a rolling-element bearing (REB) is the heart of rotating components; however, its failure can have daunting effects ranging from costly unexpected breakdown to catastrophic life-threatening situations. Consequently, the need for accurate condition monitoring and prognostics of REBs cannot be overemphasized. In view of achieving a more comprehensive condition assessment for prognostics of REBs, this study proposes a kernel principal component analysis (KPCA) feature fusion technique for degradation assessment and a deep learning model for prognostics. The deep learning method-deep long short-term memory (DLSTM) has shown an evident comparative advantage over the basic LSTM model and standard recurrent neural networks for time-series forecasting. Subsequently, the proposed prognostics model-KPCA-DLSTM performance was validated with a run-to-failure experiment on REBs and evaluated for accuracy against other prognostics methods reported in other works of literature using standard performance metrics. The proposed method was also used for REB remaining useful life (RUL) prediction and the results show that the KPCA-DLSTM does not only reflect a more monotonic bearing degradation trend but also yields better prognostics results.
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This article is based on the results of a study carried out in support of Agency for Defense Development (RAM specialized laboratory, UD180018AD).
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Ugochukwu Ejike Akpudo received his B.Eng. degree in Mechanical and Production Engineering at Enugu State University of Science and Technology, Nigeria in 2012. While working at Maclisle Complex Limited, Enugu, Nigeria from 2015 to 2019, he familiarized himself with a wide variety of efficient and effective ways of mechanical systems main-tainability, general safety engineering, reliability engineering, and loss prevention. Currently he is a graduate student and full-time researcher at Defence Reliability Laboratory, Mechanical Systems Engineering at Kumoh National Institute of Technology Gumi, South Korea. His research interests include but not limited to Prognostics and Health Management of mechatronic systems and Reliability Engineering.
Jang-Wook Hur received his Ph.D. degree in Mechanical Engineering from Tokyo Institute of Technology, Japan, in 1995. While serving in the Republic of Korea National Military, he, in 2011 ranked a Colonel and took part in various projects funded by the Korean Government like the DAPA KHP Project. From 2015 to 2020, he led the project team in the RAM program aimed at optimizing Reliability, Availability and Maintainability in the Korean Defense Systems. He is currently the H.O.D. Mechanical Systems Engineering and the Director of Defense Reliability Laboratory (an Advanced Research Center supported by the Korean Government) at Kumoh National University of Science and Technology. He is currently the Vice President of the Korean Society for Prognostics and Health Management (KSPHM). His research interest are Prognostics and Health Management and Reliability Engineering.
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Akpudo, U.E., Hur, JW. A feature fusion-based prognostics approach for rolling element bearings. J Mech Sci Technol 34, 4025–4035 (2020). https://doi.org/10.1007/s12206-020-2213-x
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DOI: https://doi.org/10.1007/s12206-020-2213-x