An Overview of Deep Learning in Prognostics and Health Management

Abstract

Deep learning has attracted intense interest recently in Prognostics and Health Management (PHM), due to its enormous representing power and capability in automated feature learning. This paper attempts to survey recent advancements of PHM methodologies associated with deep learning. After a brief introduction to several deep learning models, we reviewed and analyzed applications of fault detection, diagnosis and prognosis using deep learning, respectively. The survey reveals that most existing work utilized deep learning to conduct feature learning from unstructured raw data including vibration data, current signals, images and videos. Deep learning provides a general framework for PHM applications: fault detection uses either reconstruction error or stacks a binary classier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; and prognosis adds a continuous regression layer to predict remaining useful life. We further pointed out some challenges and potential opportunities in the field.