Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA
Aiming at the problem that the initial fault signal of rolling bearings is weak and the fault characteristic is difficult to extract, this study proposes a rolling bearing fault diagnosis method based on variational modal decomposition (VMD) for adaptive parameter optimization based on the improved...
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Editorial Office of Journal of Mechanical Transmission
2023-10-01
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| Series: | Jixie chuandong |
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| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.023 |
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| author | Yang Sen Wang Hengdi Cui Yongcun Li Chang Tang Yuanchao |
| author_facet | Yang Sen Wang Hengdi Cui Yongcun Li Chang Tang Yuanchao |
| author_sort | Yang Sen |
| collection | DOAJ |
| description | Aiming at the problem that the initial fault signal of rolling bearings is weak and the fault characteristic is difficult to extract, this study proposes a rolling bearing fault diagnosis method based on variational modal decomposition (VMD) for adaptive parameter optimization based on the improved sparrow search algorithm (SSA) and the extreme learning machine (ELM) with multi-layer feature vector fusion. Firstly, the optimization step size of SSA is adaptively changed according to the fittness function value and the number of iterations. Secondly, the improved SSA optimizes the important parameters (decomposition number <italic>K</italic> and penalty factor <italic>α</italic>) of the VMD algorithm, and the fittness function adopts the minimum envelope entropy. Thirdly, the intrinsic mode function (IMF) component with the smallest envelope spectral entropy after SSA-VMD decomposition is extracted as the optimal component, and its eigenvalue is calculated. Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%. |
| format | Article |
| id | doaj-art-97fd8b2fb42341b896e84457b5877785 |
| institution | DOAJ |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-97fd8b2fb42341b896e84457b58777852025-08-20T02:47:58ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392023-10-014716216842736816Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSAYang SenWang HengdiCui YongcunLi ChangTang YuanchaoAiming at the problem that the initial fault signal of rolling bearings is weak and the fault characteristic is difficult to extract, this study proposes a rolling bearing fault diagnosis method based on variational modal decomposition (VMD) for adaptive parameter optimization based on the improved sparrow search algorithm (SSA) and the extreme learning machine (ELM) with multi-layer feature vector fusion. Firstly, the optimization step size of SSA is adaptively changed according to the fittness function value and the number of iterations. Secondly, the improved SSA optimizes the important parameters (decomposition number <italic>K</italic> and penalty factor <italic>α</italic>) of the VMD algorithm, and the fittness function adopts the minimum envelope entropy. Thirdly, the intrinsic mode function (IMF) component with the smallest envelope spectral entropy after SSA-VMD decomposition is extracted as the optimal component, and its eigenvalue is calculated. Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.023Rolling bearingEarly fault diagnosisVariational mode decompositionImproved sparrow search algorithmCoefficient of variation methodExtreme learning machine |
| spellingShingle | Yang Sen Wang Hengdi Cui Yongcun Li Chang Tang Yuanchao Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA Jixie chuandong Rolling bearing Early fault diagnosis Variational mode decomposition Improved sparrow search algorithm Coefficient of variation method Extreme learning machine |
| title | Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA |
| title_full | Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA |
| title_fullStr | Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA |
| title_full_unstemmed | Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA |
| title_short | Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA |
| title_sort | bearing fault diagnosis based on parameter optimized vmd and elm with improved ssa |
| topic | Rolling bearing Early fault diagnosis Variational mode decomposition Improved sparrow search algorithm Coefficient of variation method Extreme learning machine |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.023 |
| work_keys_str_mv | AT yangsen bearingfaultdiagnosisbasedonparameteroptimizedvmdandelmwithimprovedssa AT wanghengdi bearingfaultdiagnosisbasedonparameteroptimizedvmdandelmwithimprovedssa AT cuiyongcun bearingfaultdiagnosisbasedonparameteroptimizedvmdandelmwithimprovedssa AT lichang bearingfaultdiagnosisbasedonparameteroptimizedvmdandelmwithimprovedssa AT tangyuanchao bearingfaultdiagnosisbasedonparameteroptimizedvmdandelmwithimprovedssa |