Remaining useful life estimation based on Wiener degradation processes with random failure threshold

• Published in: Journal of Central South University Vol. 23; no. 9; pp. 2230 - 2241
• Main Authors: Tang, Sheng-jin, Yu, Chuan-qiang, Feng, Yong-bao, Xie, Jian, Gao, Qin-he, Si, Xiao-sheng
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.09.2016; Springer Nature B.V
• Subjects: Bayesian analysis; Condition monitoring; Control Science and Information Engineering; Current distribution; Engineering; Failure; Mathematical models; Mechanical Engineering; Metallic Materials; Normal distribution; Randomness; Useful life; wiener process; bayesian; random failure threshold; remaining useful life; condition based maintenance; EM algorithm
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-016-3281-z

Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold (RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization (EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.