A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM
Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KEL...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/2108457 |
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| _version_ | 1832560970734501888 |
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| author | Yan Lu Peijiang Li |
| author_facet | Yan Lu Peijiang Li |
| author_sort | Yan Lu |
| collection | DOAJ |
| description | Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads. |
| format | Article |
| id | doaj-art-cb8ee0386fd84e9bb5ab6617be16096e |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-cb8ee0386fd84e9bb5ab6617be16096e2025-02-03T01:26:23ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/21084572108457A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELMYan Lu0Peijiang Li1Department of Information Engineering, Quzhou College of Technology, Quzhou 324000, ChinaDepartment of Information Engineering, Quzhou College of Technology, Quzhou 324000, ChinaAiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads.http://dx.doi.org/10.1155/2021/2108457 |
| spellingShingle | Yan Lu Peijiang Li A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM Shock and Vibration |
| title | A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM |
| title_full | A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM |
| title_fullStr | A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM |
| title_full_unstemmed | A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM |
| title_short | A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM |
| title_sort | novel fault diagnosis method for motor bearing based on dtcwt and afso kelm |
| url | http://dx.doi.org/10.1155/2021/2108457 |
| work_keys_str_mv | AT yanlu anovelfaultdiagnosismethodformotorbearingbasedondtcwtandafsokelm AT peijiangli anovelfaultdiagnosismethodformotorbearingbasedondtcwtandafsokelm AT yanlu novelfaultdiagnosismethodformotorbearingbasedondtcwtandafsokelm AT peijiangli novelfaultdiagnosismethodformotorbearingbasedondtcwtandafsokelm |