Hongqiang Gu, Chiming Guo, and Jianmin Zhao
Online and offline adaptive maintenance policies are presented for the systems with non-stationary Wiener degradation processes. In the policies, preventive maintenance threshold will change with the degradation indicator adaptively. The cumulative sum algorithm is used in the online model to detect the change point of the degradation process. On this basis, an analytical model of the offline model is developed. A gearbox case based on the vibration-based degradation signals is studied to show the performance of the maintenance policy. It shows that it is important to consider the change point and to be of high effectiveness using the adaptive maintenance model.
Weian Yan, Baowei Song, Zhaoyong Mao and Guilin Duan
Bayesian estimation for parameters and the reliability of products for which the performance degradation process modeled by wiener process are obtained based on linex loss function. Using both non-informative and conjugate prior distribution, several Bayesian estimates under squared error and linex loss functions are computed. Finally, these Bayesian estimates are compared through the mean squared error (MSE) based on Monte Carlo simulation study. According to these comparisons, it is shown that Bayesian estimators with linex loss function are more flexible.