Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN
ObjectiveAiming at the problem of poor accuracy of gearbox fault diagnosis under noise interference, a new fault diagnosis method for gearboxes based on the denoising methods of optimized variational modal decomposition (VMD）and non-local means (NLM) was constructed, combined with a one-dimensional...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2025-05-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.05.020 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849322509557563392 |
|---|---|
| author | WAN Zhiguo ZHAO Wei WANG Zhiguo DOU Yihua |
| author_facet | WAN Zhiguo ZHAO Wei WANG Zhiguo DOU Yihua |
| author_sort | WAN Zhiguo |
| collection | DOAJ |
| description | ObjectiveAiming at the problem of poor accuracy of gearbox fault diagnosis under noise interference, a new fault diagnosis method for gearboxes based on the denoising methods of optimized variational modal decomposition (VMD）and non-local means (NLM) was constructed, combined with a one-dimensional deep residual shrinkage network (1DDRSN).MethodsFirstly, the parameters in the VMD were automatically optimized using the subtractive average-based optimization (SABO); secondly, each intrinsic mode function (IMF） after the decomposition of the VMD was filtered using sample entropy, and the noise-containing components were subjected to the NLM denoising and reconstruction; then, a residual network that combines the attention mechanism with soft thresholding was introduced to model 1DDRSN; finally, the denoised and reconstructed signals were inputted into the 1DDRSN for fault diagnosis and identification. And the validation was carried out through the DDS test bench.ResultsThe results show that the denoised signal improves the fault accuracy by 3.16% compared with the original signal, which indicates that the optimized VMD-NLM has a better noise reduction effect. The diagnostic accuracy of the 1DDRSN reaches 99.33%, and compared with the CNN and ResNet, the accuracy improves by 5.97% and 1.17%, respectively, which verifies the method’s feasibility and the efficiency of the diagnosis effect. |
| format | Article |
| id | doaj-art-2b44e3860f3a4109a05e93b59f6eb556 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-2b44e3860f3a4109a05e93b59f6eb5562025-08-20T03:49:21ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392025-05-0149150160100921775Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSNWAN ZhiguoZHAO WeiWANG ZhiguoDOU YihuaObjectiveAiming at the problem of poor accuracy of gearbox fault diagnosis under noise interference, a new fault diagnosis method for gearboxes based on the denoising methods of optimized variational modal decomposition (VMD）and non-local means (NLM) was constructed, combined with a one-dimensional deep residual shrinkage network (1DDRSN).MethodsFirstly, the parameters in the VMD were automatically optimized using the subtractive average-based optimization (SABO); secondly, each intrinsic mode function (IMF） after the decomposition of the VMD was filtered using sample entropy, and the noise-containing components were subjected to the NLM denoising and reconstruction; then, a residual network that combines the attention mechanism with soft thresholding was introduced to model 1DDRSN; finally, the denoised and reconstructed signals were inputted into the 1DDRSN for fault diagnosis and identification. And the validation was carried out through the DDS test bench.ResultsThe results show that the denoised signal improves the fault accuracy by 3.16% compared with the original signal, which indicates that the optimized VMD-NLM has a better noise reduction effect. The diagnostic accuracy of the 1DDRSN reaches 99.33%, and compared with the CNN and ResNet, the accuracy improves by 5.97% and 1.17%, respectively, which verifies the method’s feasibility and the efficiency of the diagnosis effect.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.05.020Variational mode decompositionSubtractive average-based optimizationNon-local means de-noising1DDRSNFault diagnosis |
| spellingShingle | WAN Zhiguo ZHAO Wei WANG Zhiguo DOU Yihua Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN Jixie chuandong Variational mode decomposition Subtractive average-based optimization Non-local means de-noising 1DDRSN Fault diagnosis |
| title | Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN |
| title_full | Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN |
| title_fullStr | Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN |
| title_full_unstemmed | Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN |
| title_short | Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN |
| title_sort | gearbox fault diagnosis method based on optimized vmd nlm with 1ddrsn |
| topic | Variational mode decomposition Subtractive average-based optimization Non-local means de-noising 1DDRSN Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.05.020 |
| work_keys_str_mv | AT wanzhiguo gearboxfaultdiagnosismethodbasedonoptimizedvmdnlmwith1ddrsn AT zhaowei gearboxfaultdiagnosismethodbasedonoptimizedvmdnlmwith1ddrsn AT wangzhiguo gearboxfaultdiagnosismethodbasedonoptimizedvmdnlmwith1ddrsn AT douyihua gearboxfaultdiagnosismethodbasedonoptimizedvmdnlmwith1ddrsn |