Role of Machine Learning Approaches in Remaining Useful Prediction: A Review

Abstract

The remaining useful life prediction is the length of time a machine likely to operate before it requires a specific maintenance action. The RUL prediction on any component paves the way to schedule predictive maintenance strategies, optimize the system function, and prevent unscheduled downtime. The machine learning techniques used in RUL prediction provide an accurate prognostic environment and a better understanding of degradation and failure patterns. Those exact patterns provide the confidence bound over the prediction process. The deep learning and machine learning approaches attracted the recent predictive maintenance world for effective RUL prediction. However, recent studies have revealed the challenges in selecting particular RUL models for precise prediction. The paper provides insights for researchers on ML techniques used for remaining useful life (RUL) prediction. The comparative study reveals the concepts of different machine learning techniques optimized for RUL prediction and suggests the challenges associated with the methods.