Remaining Useful Life Prediction Methodologies With Health Indicator Dependence for Rotating Machinery: A Comprehensive Review

Abstract

Facilitated by artificial intelligence (AI) technologies such as deep learning (DL), predictive maintenance (PdM) has emerged as the most cost-effective maintenance approach for significantly enhancing the safety and availability of industrial equipment. Remaining useful life (RUL) prediction of rotating machinery (RM) is one of the core tasks of PdM, which can assist in maintenance decision-making and optimize asset allocation. Among them, the prognostic approach with health indicator (HI) as an intermediate bridge has attracted increasing attention due to its advantages in combining health condition monitoring (CM) and RUL prediction capabilities. Unfortunately, there has not been a comprehensive review to systematically analyze and summarize the recent advances in HI-dependent prognostic approaches. To fill this gap, this article comprehensively reviews the RUL prediction methods with HI dependence for RM to help researchers quickly outline the current research status and state-of-the-art methods. First, several run-to-failure datasets used to validate the performance of prognostic algorithms are briefly introduced. Second, three types of HI-based prognosis methods for RM, including similarity-based prognostic (SBP) methods, model and data-based statistical methods, and intelligent time series prediction (TSP) methods, are analyzed comprehensively. Finally, the current main challenges and prospects are presented to conclude this article. It is anticipated that the target audience of this article will be practitioners and researchers involved in the field of machinery PdM, providing them with the necessary basic knowledge and technical overview.

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