Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effecti...

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Vydané v:The Artificial intelligence review Ročník 56; číslo 5; s. 4667 - 4709
Hlavní autori: Tama, Bayu Adhi, Vania, Malinda, Lee, Seungchul, Lim, Sunghoon
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.05.2023
Springer
Springer Nature B.V
Predmet:
Artificial Intelligence
Computer Science
Condition monitoring
Deep learning
Fault diagnosis
Indexing
Learning
Machinery
Magneto-electric machines
Measurement
Medical diagnosis
Neural networks
Physics
Predictive maintenance
Rotating machinery
Technology application
Troubleshooting
Vibration analysis
Vibration measurement
Vibration monitoring
Deep learning
Fault diagnosis
Condition monitoring
Vibration sensor
Vibration signal
ISSN:0269-2821, 1573-7462
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Popis
Shrnutí:Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-022-10293-3