Prediction of Remaining Useful Life of Mechanical Equipment : A Review

Remaining Useful Life (RUL) prediction of mechanical equipment is an application of Prognostics and Health Management (PHM) technology in mechanical equipment, and is one of the most important indicators for detecting equipment failures before the equipment performance fails. This paper reviews data and model based methods for remaining useful life prediction. In order to improve the prediction accuracy, this paper proposes the new technique of constructing health factors, in which the health factors are divided into physical and virtual health factors, which is important for improving equipment reliability, reducing maintenance costs and optimizing production plans. With the development of data collection and artificial intelligence technology, a large number of domestic and foreign scholars have adopted machine learning models for RUL prediction. Therefore, the article concludes with a review of a large number of studies on the application of RUL prediction methods to rolling bearings and batteries in recent years.