Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model
The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it pr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2024/8026402 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559708943155200 |
|---|---|
| author | Wei Yuan Fuzheng Liu Hongbin Gu Fei Miao Faye Zhang Mingshun Jiang |
| author_facet | Wei Yuan Fuzheng Liu Hongbin Gu Fei Miao Faye Zhang Mingshun Jiang |
| author_sort | Wei Yuan |
| collection | DOAJ |
| description | The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals. |
| format | Article |
| id | doaj-art-9869c50b5b5040e2be40fb37dcdd954a |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-9869c50b5b5040e2be40fb37dcdd954a2025-02-03T01:29:26ZengWileyShock and Vibration1875-92032024-01-01202410.1155/2024/8026402Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM ModelWei Yuan0Fuzheng Liu1Hongbin Gu2Fei Miao3Faye Zhang4Mingshun Jiang5College of Civil AviationSchool of Control Science and EngineeringCollege of Civil AviationFlying CollegeSchool of Control Science and EngineeringSchool of Control Science and EngineeringThe current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals.http://dx.doi.org/10.1155/2024/8026402 |
| spellingShingle | Wei Yuan Fuzheng Liu Hongbin Gu Fei Miao Faye Zhang Mingshun Jiang Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model Shock and Vibration |
| title | Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model |
| title_full | Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model |
| title_fullStr | Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model |
| title_full_unstemmed | Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model |
| title_short | Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model |
| title_sort | accuracy improved fault diagnosis method for rolling bearing based on enhanced esgmd cc and ba elm model |
| url | http://dx.doi.org/10.1155/2024/8026402 |
| work_keys_str_mv | AT weiyuan accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel AT fuzhengliu accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel AT hongbingu accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel AT feimiao accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel AT fayezhang accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel AT mingshunjiang accuracyimprovedfaultdiagnosismethodforrollingbearingbasedonenhancedesgmdccandbaelmmodel |