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

TL;DR

This survey reviews how deep learning techniques like AE, CNN, and RNN are increasingly applied to machine health monitoring, highlighting recent trends and research developments in this data-driven industrial field.

Contribution

It provides a comprehensive overview of deep learning applications in machine health monitoring, summarizing recent research and discussing emerging trends and future directions.

Findings

Deep learning models improve fault detection accuracy.

Various neural network architectures are effective for different monitoring tasks.

Emerging trends include hybrid models and real-time analysis.

Abstract

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.