A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges

被引：0

作者：

Matania O. ^{[1
]}

Dattner I. ^{[2
]}

Bortman J. ^{[1
]}

Kenett R.S. ^{[3
]}

Parmet Y. ^{[4
]}

机构：

[1] PHM Laboratory, Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva

[2] Department of Statistics, University of Haifa, 199 Abba Khoushy, Haifa

[3] KPA Ltd and The Samuel Neaman Institute, Technion, Haifa

[4] Department of Industrial Engineering and management, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva

来源：

Journal of Sound and Vibration | 2024年 / 590卷

关键词：

Deep learning; Fault diagnosis; Systematic literature review; Transfer across conditions; Transfer across machines;

D O I：

10.1016/j.jsv.2024.118562

中图分类号：

学科分类号：

摘要：

Over the last decade, thousands of papers on machine-learning for diagnosing faults in rotating machinery through vibration signals have been published. Specifically, deep learning, coupled with domain adaptation, has been replacing traditional physical and signal-processing techniques. This study systematically reviews the literature on deep learning for fault diagnosis in rotating machinery, focusing on real-world cases. The review points out current limitations in systems with several examples of labeled or unlabeled faulty signals. The study concludes by suggesting directions in which deep learning can be successfully implemented, contributing to the enhancement of current diagnostic capabilities. © 2024 Elsevier Ltd

引用

共 50 条

[41] Fault diagnosis of rotating machinery based on graph data deep mining
Liu, Jie
Yang, Chaoying
Zhou, Kaibo
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49 (09): : 1 - 5
[42] Multichannel Information Fusion and Deep Transfer Learning for Rotating Machinery Fault Diagnosis
Zhang L.
Hu Y.
Zhao L.
Zhang H.
Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2023, 34 (08): : 966 - 975
[43] A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery
Zhao, Xiaoli
Jia, Minping
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2020, 19 (06): : 1745 - 1763
[44] A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Shao Haidong
Jiang Hongkai
Zhao Huiwei
Wang Fuan
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 95 : 187 - 204
[45] The vibration fault diagnosis of high-speed rotating machinery based on the Simulation
Tian, Yabin
Qi, Xueyi
Liang, Guobin
EQUIPMENT MANUFACTURING TECHNOLOGY AND AUTOMATION, PTS 1-3, 2011, 317-319 : 2172 - +
[46] Design of vibration analysis and fault diagnosis for the rotating machinery based on virtual instrument
Bao, Haitao
Zhao, Xing
Yuan, Yu
PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY APPLICATIONS (ICCITA), 2016, 53 : 80 - 83
[47] Fault diagnosis of bearings in rotating machinery based on vibration power signal autocorrelation
Sadoughi, Alireza
Tashakkor, Soheil
Ebrahimi, Mohammad
Rezaei, Esmaeil
2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13, 2006, : 2352 - +
[48] Review on deep learning classifiers for faults diagnosis of rotating industrial machinery
Shaalan, Ameer Ali
Mefteh, Wafa
Frihida, Ali Mohsen
SERVICE ORIENTED COMPUTING AND APPLICATIONS, 2024, : 361 - 379
[49] An Ensemble Learning-Based Fault Diagnosis Method for Rotating Machinery
Tian, Jing
Azarian, Michael H.
Pecht, Michael
Niu, Gang
Li, Chuan
2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN), 2017, : 96 - 101
[50] Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery
Li, Guoqiang
Wu, Jun
Deng, Chao
Xu, Xuebing
Shao, Xinyu
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2022, 27 (05) : 2796 - 2805

← 1 2 3 4 5 →