A wiener-based remaining useful life prediction method with multiple degradation patterns

Remaining useful life prediction is a critical step in prognostics and health management. Wiener process has been widely used in RUL prediction studies for degradation modelling. However, current wiener-based remaining life prediction methods usually include single degradation pattern, which limits the universality of the methods to cases with unclear degradation trends. To overcome this drawback, a novel remaining useful life prediction method considering multiple degradation patterns is developed in this paper. In the offline phase, the initial degradation parameters are estimated with an improved approximation algorithm combing least squares regression and maximum likelihood estimation. In the online phase, three degradation patterns are considered and the Bayesian inference and expectation maximization algorithm are applied to estimate the model parameters based on the online monitored degradation values. The remaining useful life prediction of each pattern is obtained as probability density function according to the property of the wiener process. To evaluate the performance of each degradation pattern and to determine the optimal pattern in real time, a fitness evaluation algorithm integrating dynamic time warping and feature-similarity is developed. The simulation signals and experimental datasets of aero engines and cutting tools are introduced to verify the effectiveness of the proposed method, and some widely used prediction methods are also applied as comparison. The results of the proposed method show higher accuracy than state-of-the art methods, which demonstrate the superiority of the proposed method.