Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors

Remaining useful life (RUL) prediction plays a crucial role in prognostics and health management (PHM). Recently, the adaptive model-based RUL prediction, which is proven effective and flexible, has gained considerable attention. Most research on adaptive degradation models focuses on the Wiener pro...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.3498-3510
Main Authors: Chen, Xudan, Sun, Xinli, Si, Xiaosheng, Li, Guodong
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive model
Algorithms
Corresponding states
Data models
Degradation
Gaussian process
inverse Gaussian process
Life prediction
Measurement errors
Parameter estimation
Particle methods (mathematics)
Predictive models
Prognostics and health management
Reliability
remaining useful life
State space models
Useful life
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Summary:Remaining useful life (RUL) prediction plays a crucial role in prognostics and health management (PHM). Recently, the adaptive model-based RUL prediction, which is proven effective and flexible, has gained considerable attention. Most research on adaptive degradation models focuses on the Wiener process. However, since the degradation process of some products is accumulated and irreversible, the inverse Gaussian (IG) process that can describe monotonic degradation paths is a natural choice for degradation modelling. This article proposes a nonlinear adaptive IG process along with the corresponding state space model considering measurement errors. Then, an improved particle filtering algorithm is presented to update the degradation parameter and estimate the underlying degradation state under the nonGaussian assumptions in the state space model. The RUL prediction depending on historical degradation data is derived based on the results of particle methods, which can avoid high-dimensional integration. In addition, the expectation-maximization (EM) algorithm combined with an improved particle smoother is developed to estimate and adaptively update the unknown model parameters once newly monitored degradation data become available. Finally, this article concludes with a simulation study and a case application to demonstrate the applicability and superiority of the proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2961951