Prediction-Interval-Based Credibility Criteria of Prognostics Results for Practical Use

Abstract

Prognostics is an AI-based technique for predicting the degrading/damaging behavior and remaining useful life (RUL) of a system, which facilitates a cost-effective and smart maintenance process. Many prognostics methods have been developed for various applications, such as bearings, aircraft engines, batteries, and fuel cell stacks. Once a new prognostics method is developed, it is evaluated using several metrics based on the true value of the RUL. However, these typical evaluation metrics are not applicable in real-world applications, as the true RUL cannot be known before the actual failure of a system. There are no ways to determine the reliability of prognostics results in practice. Therefore, this article presents the credibility criteria of prognostics results based on prediction intervals (PI), which are known values, unlike the true RUL. The PI-based credibility criteria for prognostics results are explained with two simple examples under different levels of noise to help with the decision making on prognostics results in the industrial field.