Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point. (July 2023)

• Record Type: Journal Article
• Authors: Li, Guofa; Wei, Jingfeng; He, Jialong; Yang, Haiji; Meng, Fanning
• Is Part Of: Reliability engineering & system safety. Volume 235(2023)
• Publication Date: 2023-07
• Subjects: Implicit Kalman filtering method -- Remaining useful life -- Time to start prediction -- Variational Bayesian -- Rolling bearings; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2023.109269
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A prediction method with change-point detection for time series data. Adopt the variational Bayesian technique to construct state-space model. Use multi-channel signals to detect degradation stage transition point for bearings. The proposed method has superiority in initial and long-term prediction accuracy. Abstract: Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the research area of RUL prediction. However, three dispiriting shortcomings in KF methods are still unavoidable, including: (1) difficulty in tracking the unknown time-varying noise information, (2) the subjectivity for setting time to start prediction (TSP), and (3) short-term accuracy of the predicting results based on linear predictors. To improve the capability of KF methods, this work adopts the variational Bayesian technique to adaptively describe noise information and considers linear and nonlinear factors of multi-channel signals to recognize the degradation stage transition point of bearing as TSP. Moreover, this work proposes an implicit Kalman filtering method to predict the RUL. The effectiveness of the proposed method is validated on XJTU-SY and IMS-Rexnord bearing data. Results show that the proposed method can recognize the TSP and improve the long-term accuracy of the prediction result during the accelerated degradation stage.