A Prognostic Model for Stochastic Degrading Systems With State Recovery: Application to Li-Ion Batteries

• Published in: IEEE transactions on reliability 2017-12, Vol.66 (4), p.1293-1308
• Main Authors: Zheng-Xin Zhang, Xiao-Sheng Si, Chang-Hua Hu, Pecht, Michael G.
• Format: Article
• Language: English
• Subjects: Degradation; Degradation model; Markov processes; Numerical models; Performance evaluation; prognostics; Prognostics and health management; recovery; remaining useful life (RUL); semi-Markov model (SMM); Stochastic processes
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2017.2742298

Many industrial systems inevitably suffer performance degradation. Thus, predicting the remaining useful life (RUL) for such degrading systems has attracted significant attention in the prognostics community. For some systems like batteries, one commonly encountered phenomenon is that the system performance degrades with usage and recovers in storage. However, almost all of the current prognostic studies do not consider such a recovery phenomenon in stochastic degradation modeling. In this paper, we present a prognostic model for deteriorating systems experiencing a switching operating process between usage and storage, where the system degradation state recovers randomly after the storage process. The possible recovery from the current time to the predicted future failure time is incorporated in the prognosis. First, the degradation state evolution of the system is modeled through a diffusion process with piecewise but time-dependent drift coefficient functions. Under the concept of first hitting time, we derived the lifetime and RUL distributions for systems with specific constant working mode. Further, we extended the results of RUL distribution in specific constant working mode to the case of stochastic working mode, which is modeled through a flexible two-state semi-Markov model (SMM) with phase-type distributed interval times. The unknown parameters in the present model are estimated based on the observed condition monitoring data of the system, and the SMM model is identified on the basis of the operating data. A numerical study and a case study of Li-ion batteries are carried out to illustrate and demonstrate the proposed prognostic method. Experimental results indicate that the presented method can improve the accuracy of lifetime and RUL estimation for systems with state recovery.