A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability. (August 2021)

• Record Type: Journal Article
• Authors: Yu, Wennian; Tu, Wenbing; Kim, Il Yong; Mechefske, Chris
• Is Part Of: Reliability engineering & system safety. Volume 212(2021)
• Publication Date: 2021-08
• Subjects: Remaining useful life -- Wiener process -- Nonlinear degradation -- Proportional parameter; 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.2021.107631
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A nonlinear-drift-driven wiener process degradation model considering three sources of variability. The analytical expression for the probability distribution function of RUL is derived. The proposed model is general and covers the previous simplified models. A down-sampling strategy is introduced to avoid the numerical underflow issue. Abstract: The estimation of remaining useful life (RUL) for a degrading system has gained increasing attention both in academia and in industry for decades. In this study, a nonlinear-drift-driven Wiener process model considering three common sources of uncertainty is constructed by an age-dependent state-space model for the RUL estimation of degrading systems. Analytical expressions approximating the probability distribution function of RUL of the above-described model are derived for both online estimation and offline estimation scenarios. It is shown that the derived expressions are more general and cover the simplified cases discussed in previous woks. A model parameter estimation method is proposed based on unbalanced historical degradation measurements, and a down-sampling strategy is introduced to avert the underflow issue. The prognostic performance of the proposed method against previous similar works under the online and offline estimation scenarios is demonstrated on two publicly available datasets by comparisons in terms of three prognostic metrics and the probabilistic distribution of RUL at different condition monitoring points. The results show that it is necessary to include the nonlinear degradation characteristics and the three sources of uncertainty into the RUL estimation especially for the offline estimation scenario.