A survey on Deep Learning based bearing fault diagnosis

Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units, Deep Learning has become a promising tool for intelligent bearing fault diagnosis. This survey paper intends to provide a systematic review of Deep Learning based bearing fault diagnosis. The three popular Deep Learning algorithms for bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced. And their applications are reviewed through publications and research works on the area of bearing fault diagnosis. Further applications and challenges in this research area are also discussed. (C) 2018 Elsevier B.V. All rights reserved.