A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

238 Scopus Citations
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Author(s)

  • Yu Wang
  • Yizhen Peng
  • Yanyang Zi
  • Xiaohang Jin
  • Kwok-Leung Tsui

Detail(s)

Original languageEnglish
Article number7420685
Pages (from-to)924-932
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume12
Issue number3
Online published25 Feb 2016
Publication statusPublished - Jun 2016

Abstract

Prognostics of the remaining useful life (RUL) has emerged as a critical technique for ensuring the safety, availability, and efficiency of a complex system. To gain a better prognostic result, degradation information is quite useful because it can reflect the health status of a system. However, due to the lack of accurate information about the plants' degradation, the prognostic model is usually not well established. To solve this problem, this paper proposes a two-stage strategy that is in the context of data-driven modeling to predict the future health status of a bearing, where the degradation information was estimated by calculating the deviation of multiple statistics of vibration signals of a bearing from a known healthy state. Then, a prediction stage based on an enhanced Kalman filter and an expectation-maximization algorithm were used to estimate the RUL of the bearing adaptively. To verify the effectiveness of the proposed approach, a real-bearing degradation problem was implemented.

Research Area(s)

  • Degradation, Kalman filter (KF), prognostics, remaining useful life (RUL) estimation

Citation Format(s)

A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem. / Wang, Yu; Peng, Yizhen; Zi, Yanyang et al.

In: IEEE Transactions on Industrial Informatics, Vol. 12, No. 3, 7420685, 06.2016, p. 924-932.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review