Remaining useful life prediction for degradation processes based on the Wiener process considering parameter dependence

Remaining useful life prediction (RUL) is a critical procedure in the application of prognostics and health management for devices or systems. It is difficult to predict the RUL in a time-varying external environment. Specifically, many mechanical systems typically experience various operating conditions, which have impacts on the degradation process and degradation rate. In particular, the linear degradation modeling of the Wiener process-based RUL prediction method has attracted considerable attention recently. However, the dependency of degradation rate and operating conditions is generally ignored in the current degradation modeling, which leads to inaccurate issues in the RUL prediction. Therefore, to solve the above issues, a novel RUL prediction method based on the Wiener process considering parameter dependence is proposed in this paper. At first, a linear Wiener process degradation model considering parameter dependence is constructed to describe the dependency of the drift coefficient and operating conditions. Secondly, the probability density function of RUL is derived under the concept of first hit time. After that, the collaboration between the Bayesian update and expectation maximization algorithm is introduced to update and estimate the model parameters. Finally, the validity and applicability of the proposed method are verified by a numerical simulation and three case studies of bearings.

Automated summary

This paper proposes a new RUL prediction method based on the Wiener process, considering parameter dependence to solve the issue of ignoring the dependency between degradation rate and operating conditions in current degradation modeling. By constructing a linear Wiener process degradation model to describe the parameter dependence, the probability density function of RUL is derived, and the Bayesian update and expectation maximization algorithm are introduced to update and estimate the model parameters. The validity and applicability of the proposed method are verified through numerical simulation and case studies of bearings.