Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition
Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural netwo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/6542913 |
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| _version_ | 1832553958875332608 |
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| author | Dong Liu Xu Lai Zhihuai Xiao Dong Liu Xiao Hu Pei Zhang |
| author_facet | Dong Liu Xu Lai Zhihuai Xiao Dong Liu Xiao Hu Pei Zhang |
| author_sort | Dong Liu |
| collection | DOAJ |
| description | Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods. |
| format | Article |
| id | doaj-art-95ab5039582a4aceb525fc014f97703b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-95ab5039582a4aceb525fc014f97703b2025-02-03T05:52:42ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/65429136542913Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value DecompositionDong Liu0Xu Lai1Zhihuai Xiao2Dong Liu3Xiao Hu4Pei Zhang5State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, ChinaHunan Wuling Power Technology Company, Changsha 410000, ChinaVibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.http://dx.doi.org/10.1155/2020/6542913 |
| spellingShingle | Dong Liu Xu Lai Zhihuai Xiao Dong Liu Xiao Hu Pei Zhang Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition Shock and Vibration |
| title | Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition |
| title_full | Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition |
| title_fullStr | Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition |
| title_full_unstemmed | Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition |
| title_short | Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition |
| title_sort | fault diagnosis of rotating machinery based on convolutional neural network and singular value decomposition |
| url | http://dx.doi.org/10.1155/2020/6542913 |
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