Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network
As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) h...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023884?viewType=HTML |
| _version_ | 1797529038680489984 |
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| author | Qiushi Wang Zhicheng Sun Yueming Zhu Chunhe Song Dong Li |
| author_facet | Qiushi Wang Zhicheng Sun Yueming Zhu Chunhe Song Dong Li |
| author_sort | Qiushi Wang |
| collection | DOAJ |
| description | As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities. |
| first_indexed | 2024-03-10T10:07:48Z |
| format | Article |
| id | doaj.art-b7d69a3c93594ff2bd6001d9e178ec93 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-10T10:07:48Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b7d69a3c93594ff2bd6001d9e178ec932023-11-22T01:23:46ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012011199631998210.3934/mbe.2023884Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural networkQiushi Wang0Zhicheng Sun 1Yueming Zhu2Chunhe Song3Dong Li42. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China4. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China4. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China4. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China No.114 Nanta Street, Shenyang, Liaoning Province, China 2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China No.114 Nanta Street, Shenyang, Liaoning Province, China 2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, ChinaAs an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities.https://www.aimspress.com/article/doi/10.3934/mbe.2023884?viewType=HTMLfault diagnosisde-gworolling bearingattention mechanism1dcnn |
| spellingShingle | Qiushi Wang Zhicheng Sun Yueming Zhu Chunhe Song Dong Li Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network Mathematical Biosciences and Engineering fault diagnosis de-gwo rolling bearing attention mechanism 1dcnn |
| title | Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| title_full | Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| title_fullStr | Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| title_full_unstemmed | Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| title_short | Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| title_sort | intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network |
| topic | fault diagnosis de-gwo rolling bearing attention mechanism 1dcnn |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023884?viewType=HTML |
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