Wang, Zhijie, Zhai, Qingqing, and Shen, Lijuan (2024) Degradation Modeling and RUL Prediction in Dynamic Environments Using a Wiener Process With an Autoregressive Rate. IEEE Transactions on Reliability, 73. pp. 912-921.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

Since the degradation process is dependent on the environmental stresses, degrading products operating under dynamic environments can have time-varying degradation rates. Existing studies generally exploit a random walk to model the time-varying degradation rate, considering the randomness of the environmental effects. The random walk is not stationary, while the real environments, although dynamic, are often stationary. The degradation process under a stationary environment would have a stationary degradation rate. Therefore, instead of the random walk, we propose to model the stochastic degradation rate by an autoregressive model. The autoregressive rate can accommodate the randomness and stationarity of the environmental effects. Conditional on the autoregressive degradation rate, a Wiener process is used to model the degradation process. We develop an Expectation-Maximization algorithm to perform maximum likelihood estimation of model parameters. Moreover, to facilitate remaining useful life prediction, we derive the explicit probability density function for the remaining useful life (RUL). We validate the proposed model by a simulation study and justify the applicability and performance of the proposed model by two real degradation datasets.

Item ID:	93142
Item Type:	Article (Research - C1)
ISSN:	1558-1721
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	10 Aug 2026 08:02
FoR Codes:	40 ENGINEERING > 4010 Engineering practice and education > 401006 Systems engineering @ 60% 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490510 Stochastic analysis and modelling @ 40%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
Downloads:	Total: 1 Last 12 Months: 1
	More Statistics