An Adaptive Lévy Process Model for Remaining Useful Life Prediction

Predicting remaining useful life (RUL) is a crucial part of prognostics and health management (PHM), which has attracted widespread attention in academia and industry over the past few decades. Effective estimation of RUL is predicated on the development of a suitable degradation model for the syste...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-10
Main Authors: Bincheng, Wen, Mingqing, Xiao, Xilang, Tang, Jianfeng, Li, Haizhen, Zhu
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive
Data models
Degradation
Estimation
Kalman filter
Kalman filters
Life prediction
Lévy process
Mathematical models
Milling machines
Numerical controls
Parameter uncertainty
Power amplifiers
prediction
Predictive models
Probability density functions
remaining useful life (RUL)
Stochastic processes
Useful life
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Summary:Predicting remaining useful life (RUL) is a crucial part of prognostics and health management (PHM), which has attracted widespread attention in academia and industry over the past few decades. Effective estimation of RUL is predicated on the development of a suitable degradation model for the system. However, most of the existing models require offline learning of a priori parameters, which is not applicable in the absence of historical data. To solve this problem, a stochastic degradation model based on the Lévy process is proposed in this study. The utilization of the Kalman filter and expectation-maximization in conjunction with Rauch-Tung-Striebel (KF-EM-RTS) enables the acquisition and real-time updating of model parameters and the probability density function (pdf) for RUL. This approach also allows for the characterization of parameter uncertainty. Simultaneously, the analytical expression of the RUL pdf can be obtained by the definition based on first hitting time (FHT) and Lévy-Khinchin formula. The high-speed computer numerical control (CNC) milling machine cutters dataset from the 2010 IEEE Data Challenge and solid-state RF power amplifier (SSRFPA) degradation data are utilized to validate the proposed model. The results of the experiments demonstrate that the proposed model is capable of delivering accurate RUL estimations.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3332936