Statistical Modeling of Bearing Degradation Signals

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

81 Scopus Citations
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Author(s)

  • Dong Wang
  • Kwok-Leung Tsui

Detail(s)

Original languageEnglish
Pages (from-to)1331-1344
Journal / PublicationIEEE Transactions on Reliability
Volume66
Issue number4
Online published31 Aug 2017
Publication statusPublished - Dec 2017

Abstract

Bearings are the most common mechanical components used in machinery to support rotating shafts. Due to harsh working conditions, bearing performance deteriorates over time. To prevent any unexpected machinery breakdowns caused by bearing failures, statistical modeling of bearing degradation signals should be immediately conducted. In this paper, given observations of a health indicator, a statistical model of bearing degradation signals is proposed to describe two distinct stages existing in bearing degradation. More specifically, statistical modeling of Stage I aims to detect the first change point caused by an early bearing defect, and then statistical modeling of Stage II aims to predict bearing remaining useful life. More importantly, an underlying assumption used in the early work of Gebraeel et al. is discovered and reported in this paper. The work of Gebraeel et al. is extended to a more general prognostic method. Simulation and experimental case studies are investigated to illustrate how the proposed model works. Comparisons with the statistical model proposed by Gebraeel et al. for bearing remaining useful life prediction are conducted to highlight the superiority of the proposed statistical model.

Research Area(s)

  • Bayesian inference, bearing degradation, Degradation, health indicator, Machinery, Mathematical model, Modeling, Predictive models, remaining useful life (RUL), Resonant frequency, Root mean square, statistical modeling

Citation Format(s)

Statistical Modeling of Bearing Degradation Signals. / Wang, Dong; Tsui, Kwok-Leung.
In: IEEE Transactions on Reliability, Vol. 66, No. 4, 12.2017, p. 1331-1344.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review