Stochastic prognostics under multiple time-varying environmental factors

• Published in: Reliability engineering & system safety Vol. 215; p. 107877
• Main Authors: Jahani, Salman, Zhou, Shiyu, Veeramani, Dharmaraj
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.11.2021; Elsevier BV
• Subjects: B-spline regression; Bayesian analysis; Bayesian updating; Brownian motion; Condition monitoring; Degradation; Degradation modeling; Drift; Environment models; Environmental conditions; Environmental covariates; Environmental factors; Mathematical models; Relative humidity; Reliability engineering; Remaining useful life estimation; Signal monitoring; Useful life; Bayesian updating; Environmental covariates; Remaining useful life estimation; B-spline regression; Degradation modeling
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2021.107877

Prediction of the remaining useful life of in-field components, traditionally, relies on condition monitoring signals which are correlated with the physical degradation of the system. Many models assume that condition monitoring signals behave under similar environmental conditions (e.g. pressure, temperature, workload and relative humidity) or these conditions have no effect on degradation process. In this paper, we propose a Brownian motion process with a stress-dependent drift to model multiple time-varying environmental covariates. A semiparametric regression approach utilizing penalized splines is, further, proposed to model the environmental covariates-drift relationship. The unique feature of our approach is that it does not assume a functional form for the degradation process drift and models multiple environmental covariates’ effect on the degradation process. Moreover, the model is combined with in situ degradation measurements of the in-field unit and its environmental conditions to predict the unit’s remaining useful life through a Bayesian updating scheme. The performance of the proposed framework is investigated and benchmarked through analysis based on numerical studies and a case study using real-world data of frying oil degradation collected from connected fryers. •Prognosis method capturing time-varying environmental factors is proposed.•The method considers unit-to-unit variability of covariates.•Efficient procedure based on Expected-Maximization algorithm is developed.•Results are reported in both simulation studies and a real world case study.