Paper Info
Title
Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors.
Abstract
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.
Year
DOI
Venue
2020
10.1109/ACCESS.2019.2961951
IEEE ACCESS
Keywords
Field
DocType
Adaptive model,inverse Gaussian process,measurement errors,remaining useful life
Inverse Gaussian distribution,Computer science,Algorithm,Degradation process,Observational error,Distributed computing
Journal
Volume
ISSN
Citations 
8
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xudan Chen100.34
Xinli Sun200.34
Xiao-Sheng Si362346.17
Guodong Li400.34