Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important signi...

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Bibliographic Details
Published in:Shock and vibration 2021, Vol.2021 (1)
Main Authors: Cui, Wei, Meng, Guoying, Wang, Aiming, Zhang, Xinge, Ding, Jun
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Artificial intelligence
Artificial neural networks
Belief networks
Big data
Deep learning
Failure
Fault diagnosis
Generative adversarial networks
Industrial production
Machine learning
Machinery
Neural networks
Noise
Performance evaluation
Recurrent neural networks
Rotating machinery
Sensors
Signal processing
Software
Working conditions
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Summary:With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.
ISSN:1070-9622
1875-9203
DOI:10.1155/2021/3083190