# Deep Learning and Its Applications to Machine Health Monitoring: A Survey

arXiv: https://arxiv.org/abs/1612.07640 · HTML: https://mlsanity.com/paper/1612.07640/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [A Simple Structure For Building A Robust Model](https://mlsanity.com/paper/2204.11596/) (2022)
- [Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection](https://mlsanity.com/paper/2110.01447/) (2021)
- [An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods](https://mlsanity.com/paper/2309.12765/) (2023)
- [Application of a Dense Fusion Attention Network in Fault Diagnosis of Centrifugal Fan](https://mlsanity.com/paper/2311.07614/) (2023)

## Abstract (arXiv)

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.