Adaptive relevant vector machine based RUL prediction under uncertain conditions. (April 2019) - Wang, Xiuli; Jiang, Bin; Lu, Ningyun
Abstract: Engineering systems often suffer with many uncertainties during their performance degradation processes, such as the inherent uncertainties associated with the degradation progression over time and the inevitable uncertainties caused by change of loading, operation and usage conditions. In order to improve the accuracy of remaining useful life (RUL) prediction, this study takes these common uncertainties into consideration via an improved relevance vector machine (RVM) approach, which can describe accurately the degradation process from fault to failure. Firstly, based on historical data, a multi-step RVM regression model is established offline, in which the uncertainties are represented by the variances of Gaussian distributions of parameters and then are quantified as time-varying variables. Then, an adaptive RVM model is trained and the time-varying variables are updated by the expectation–maximization (EM) algorithm. For on-line prediction, given the real-time data, the RUL is forecasted by the first hitting time (FHT) method in probability perspective. The proposed method is demonstrated by two case studies on a high-speed train's traction system. The results can show the effectiveness of the proposed method. Highlights: Uncertainties are quantified as time-varying variances of Gaussian distributions. An adaptive RVM model is developed as hyperparameters are updated by EM method. On-line RUL prediction is fulfilled in a probability form of FHT.