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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/3083190 |
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| _version_ | 1832556356197941248 |
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| author | Wei Cui Guoying Meng Aiming Wang Xinge Zhang Jun Ding |
| author_facet | Wei Cui Guoying Meng Aiming Wang Xinge Zhang Jun Ding |
| author_sort | Wei Cui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b90a451e5aa24c6b801aeef2d7b7eb58 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b90a451e5aa24c6b801aeef2d7b7eb582025-02-03T05:45:37ZengWileyShock and Vibration1875-92032021-01-01202110.1155/2021/3083190Application of Rotating Machinery Fault Diagnosis Based on Deep LearningWei Cui0Guoying Meng1Aiming Wang2Xinge Zhang3Jun Ding4School of Mechanical Electronic and Information EngineeringSchool of Mechanical Electronic and Information EngineeringSchool of Mechanical Electronic and Information EngineeringSchool of Mechanical Electronic and Information EngineeringSchool of Mechanical and Electrical EngineeringWith 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.http://dx.doi.org/10.1155/2021/3083190 |
| spellingShingle | Wei Cui Guoying Meng Aiming Wang Xinge Zhang Jun Ding Application of Rotating Machinery Fault Diagnosis Based on Deep Learning Shock and Vibration |
| title | Application of Rotating Machinery Fault Diagnosis Based on Deep Learning |
| title_full | Application of Rotating Machinery Fault Diagnosis Based on Deep Learning |
| title_fullStr | Application of Rotating Machinery Fault Diagnosis Based on Deep Learning |
| title_full_unstemmed | Application of Rotating Machinery Fault Diagnosis Based on Deep Learning |
| title_short | Application of Rotating Machinery Fault Diagnosis Based on Deep Learning |
| title_sort | application of rotating machinery fault diagnosis based on deep learning |
| url | http://dx.doi.org/10.1155/2021/3083190 |
| work_keys_str_mv | AT weicui applicationofrotatingmachineryfaultdiagnosisbasedondeeplearning AT guoyingmeng applicationofrotatingmachineryfaultdiagnosisbasedondeeplearning AT aimingwang applicationofrotatingmachineryfaultdiagnosisbasedondeeplearning AT xingezhang applicationofrotatingmachineryfaultdiagnosisbasedondeeplearning AT junding applicationofrotatingmachineryfaultdiagnosisbasedondeeplearning |