A feature fusion-based prognostics approach for rolling element bearings
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DOI 10.1007/s12206-020-2213-x
Journal of Mechanical Science and Technology 34 (10) 2020 Original Article DOI 10.1007/s12206-020-2213-x Keywords: · Long short-term memory · Kernel principal component analysis · Bearing degradation assessment · Feature fusion · Remaining useful life · KPCA-DLSTM
Correspondence to: Jang-Wook Hur [email protected]
Citation: Akpudo, U. E., Hur, J.-W. (2020). A feature fusion-based prognostics approach for rolling element bearings. Journal of Mechanical Science and Technology 34 (10) (2020) ?~?. http://doi.org/10.1007/s12206-020-2213-x
Received April 6th, 2020 Revised
May 21st, 2020
Accepted June 10th, 2020 † This paper was presented at ICMR2019, Maison Glad Jeju, Jeju, Korea, November 27-29, 2019. Recommended by Guest Editor Insu Jeon
A feature fusion-based prognostics approach for rolling element bearings Ugochukwu Ejike Akpudo and Jang-Wook Hur Department of Mechanical Systems Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (yangho-dong), Gumi, Gyeongbuk 39177, Korea
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.
1. Introduction
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
The emerging concept of reliability has shown evident exponential growth against basic reactive condition-based maintenance (CBM) approaches and is showing strong dependence on the more robust big data-driven prognostics and health management (PHM) approaches which are deeply rooted in big data analytics, statistical model-based, and data-driven prognostics methods [1]. Over the years, the need for maintenance has motivated the advancement f
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