Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods

Degradation-based modeling methods have been recognized as an essential and effective approach for lifetime and remaining useful life (RUL) estimations for various health management activities that can be scheduled to ensure reliable, safe, and economical operation of deteriorating systems. As one of the most popular stochastic modeling methods, the previous several decades have witnessed remarkable developments and extensive applications of Wiener-process-based methods. However, there is no systematic review particularly focused on this topic. Therefore, this paper reviews recent modeling developments of the Wiener-process-based methods for degradation data analysis and RUL estimation, as well as their applications in the field of prognostics and health management (PHM). After a brief introduction of conventional Wiener-process-based degradation models, we pay particular attention to variants of the Wiener process by considering nonlinearity, multi-source variability, covariates, and multivariate involved in the degradation processes. In addition, we discuss the applications of the Wiener-process-based models for degradation test design and optimal decision-making activities such as inspection, condition-based maintenance (CBM), and replacement. Finally, we highlight several future challenges deserving further studies.