Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-the-art data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

Automated summary

Early detection and timely maintenance scheduling can minimize risk and improve the lifespan, reliability, and availability of a system. Two main data-driven approaches, direct calculation and indirect analysis, are used to determine the Remaining Useful Life (RUL) in predictive maintenance. This study reviews the state-of-the-art data-driven methods for RUL prediction, discussing their capabilities, scalability, performance, weaknesses, current challenges, and future directions.