Deep Learning Approaches to Remaining Useful Life Prediction: A Survey

Abstract

Prognostic and Health Management (PHM) systems have multiple facets one would need to perfect for an efficient system. One of these is the prediction of remaining useful life (RUL), which is the task of producing a number of time units (cycles, minutes, days, etc) until a part of the system or the system as a whole will fail. Over the years, deep learning approaches have been used to effectively perform this task, and these approaches fall into multiple different types of deep learning architectures. While non deep learning approaches exist, this paper focuses on a number of different deep learning approaches to solving the problem of RUL prediction.