Citation: Qiu, S.; Cui, X.; Ping, Z.;
Shan, N.; Li, Z.; Bao, X.; Xu, X. Deep
Learning Techniques in Intelligent
Fault Diagnosis and Prognosis for
Industrial Systems: A Review.
Sensors 2023, 23, 1305.
https://doi.org/10.3390/
s23031305
Academic Editor: Jongmyon Kim
Received: 26 November 2022
Revised: 23 December 2022
Accepted: 18 January 2023
Published: 23 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Deep Learning Techniques in Intelligent Fault Diagnosis and
Prognosis for Industrial Systems: A Review
Shaohua Qiu , Xiaopeng Cui, Zuowei Ping *, Nanliang Shan, Zhong Li, Xianqiang Bao and Xinghua Xu
National Key Laboratory of Science and Technology on Vessel Integrated Power System,
Naval University of Engineering, Wuhan 430033, China
* Correspondence: pingzuowei@hust.edu.cn
Abstract:
Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to take
appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent
years, deep learning methods are being widely introduced into FDP due to the powerful feature
representation ability, and its rapid development is bringing new opportunities to the promotion
of FDP. In order to facilitate the related research, we give a summary of recent advances in deep
learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are
firstly given. Seven commonly used deep learning architectures, especially the emerging generative
adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights
into the challenges in current applications of deep learning-based methods from four different aspects
of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation,
and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for
further research into the problem of intelligent industrial FDP for the community.
Keywords: fault diagnosis; fault prognosis; machine learning; deep learning; industrial systems
1. Introduction
1.1. Background
Industrial systems are typical complex systems with various subsystems and device
types of mechanical system, power system, information system, electronic system, or their
combinations. They are playing an increasingly important role in the economy, such as
manufacturing industry, energy industry and chemical industry, which are now developed
with more functions, more sophisticated structures, and larger scales [
1
]. Reliability issues
have gradually become the key of whether many modern industrial systems can be truly
practical. Once a failure occurs, it may affect the safe and stable operation of the entire sys-
tem, i.e., reducing the efficiency of the system, and causing system breakdown or damage
in severe cases [
2
]. It may also endanger personnel safety, and cause other catastrophic
consequences. Therefore, the early identification of faults in advance can greatly help to
take appropriate actions of maintenance to avoid the undesired consequences.
Driven by demand, prognostics and health management (PHM) [
3
] technology, firstly
originated from engine health monitoring systems [
4
], has gained increasingly more at-
tention. PHM is an expansion of the traditional reliability or predictive maintenance
concept oriented for complex industrial systems. It realizes the development from the
initial condition monitoring and fault diagnosis that aims to estimate health status, to
health management that aims at formulating the countermeasures based on the results of
monitoring, diagnosis, and prognosis.
In practical scenes, it is often difficult or even impossible to establish mathematical
models of complex components or systems [
5
], in order to trace and analyze faults. There-
fore, a large amount of historical data that were collected in the process of system operation
Sensors 2023, 23, 1305. https://doi.org/10.3390/s23031305 https://www.mdpi.com/journal/sensors
