Real-Time Evaluation of the Credibility of Remaining Useful Life Prediction Result

Remaining useful life (RUL) prediction links prognostic and predictive maintenance (PdM) decision-making. Since the prediction result serve as the basis for subsequent decision-making, it is vital to evaluate the performance of RUL prediction result. The most popular method tends to compares the actual RUL or run-to-failure data with the prediction result. However, in many real-world applications, such ground-truth measurements are unavailable during the prediction process. To address this issue, we propose a new method for real-time credibility evaluation of RUL prediction result in the absence of ground-truth measurements. The proposed method informs decision-makers of the confidence level of RUL prediction result prior to making a PdM decision. Multiple evaluation factors, such as accuracy, consistency, and effectiveness related to credibility, are proposed to utilize the information available during the prediction process based on the underlying stochastic process. Especially, a fuzzy comprehensive evaluation method is used to determine the credibility of RUL prediction result by comprehensively considering the evaluation factors, and an entropy weight method is used to update the weight of each factor. The rationality of the proposed credibility evaluation method is demonstrated using simulation and a benchmark dataset from NASA.