  Since 2006, deep learning (DL) has become a rapidly growing research
direction, redefining state-of-the-art performances in a wide range of areas
such as object recognition, image segmentation, speech recognition and machine
translation. In modern manufacturing systems, data-driven machine health
monitoring is gaining in popularity due to the widespread deployment of
low-cost sensors and their connection to the Internet. Meanwhile, deep learning
provides useful tools for processing and analyzing these big machinery data.
The main purpose of this paper is to review and summarize the emerging research
work of deep learning on machine health monitoring. After the brief
introduction of deep learning techniques, the applications of deep learning in
machine health monitoring systems are reviewed mainly from the following
aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and
its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines
(DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
Finally, some new trends of DL-based machine health monitoring methods are
discussed.
