Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels
Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can class...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/3059230 |
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| _version_ | 1849398827813961728 |
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| author | Fan Xu Yan jun Fang Dong Wang Jia qi Liang Kwok Leung Tsui |
| author_facet | Fan Xu Yan jun Fang Dong Wang Jia qi Liang Kwok Leung Tsui |
| author_sort | Fan Xu |
| collection | DOAJ |
| description | Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM. |
| format | Article |
| id | doaj-art-9f9a0da15a3e44b8aef242dc893b98a2 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-9f9a0da15a3e44b8aef242dc893b98a22025-08-20T03:38:30ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/30592303059230Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data LabelsFan Xu0Yan jun Fang1Dong Wang2Jia qi Liang3Kwok Leung Tsui4School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong KongDepartment of Automation, School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei, ChinaSchool of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong KongDepartment of Automation, School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei, ChinaSchool of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong KongBecause deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.http://dx.doi.org/10.1155/2018/3059230 |
| spellingShingle | Fan Xu Yan jun Fang Dong Wang Jia qi Liang Kwok Leung Tsui Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels Shock and Vibration |
| title | Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels |
| title_full | Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels |
| title_fullStr | Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels |
| title_full_unstemmed | Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels |
| title_short | Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels |
| title_sort | combining dbn and fcm for fault diagnosis of roller element bearings without using data labels |
| url | http://dx.doi.org/10.1155/2018/3059230 |
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