Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM
The vibration signal of crane gearbox has the characteristics of low signal-to-noise ratio and nonlinearity，so it needs some professional knowledge and experience to realize fault diagnosis. In order to realize intelligent fault diagnosis of crane gearbox，an intelligent fault diagnosis method based...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-04-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.018 |
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| author | Wubang Yang Bingpeng Gao Fei Chen Xinghe Zhang Weidong Ma |
| author_facet | Wubang Yang Bingpeng Gao Fei Chen Xinghe Zhang Weidong Ma |
| author_sort | Wubang Yang |
| collection | DOAJ |
| description | The vibration signal of crane gearbox has the characteristics of low signal-to-noise ratio and nonlinearity，so it needs some professional knowledge and experience to realize fault diagnosis. In order to realize intelligent fault diagnosis of crane gearbox，an intelligent fault diagnosis method based on variational modal decomposition（VMD） improved wavelet denoising and particle swarm optimization（PSO） support vector machine（SVM） is proposed. Firstly， VMD is used to decompose the vibration signal to obtain the intrinsic mode function（IMF） of different scales. The decomposed high frequency component is improved after wavelet de-noising and the low frequency component is reconstructed. Then the feature parameters of reconstructed signal are extracted to construct the feature vector， and kernel principal component analysis（KPCA） is used to realize the feature information fusion. Finally， the PSO optimized SVM is used for fault identification and classification. The experimental results show that the SVM model based on VMD improved wavelet signal preprocessing and PSO algorithm has high recognition accuracy and can effectively and accurately identify and classify the fault types of the crane gearbox. |
| format | Article |
| id | doaj-art-ee57df9a900448d1b843e2e6283167ec |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-04-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-ee57df9a900448d1b843e2e6283167ec2025-01-10T14:53:46ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-04-01451051118814184Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVMWubang YangBingpeng GaoFei ChenXinghe ZhangWeidong MaThe vibration signal of crane gearbox has the characteristics of low signal-to-noise ratio and nonlinearity，so it needs some professional knowledge and experience to realize fault diagnosis. In order to realize intelligent fault diagnosis of crane gearbox，an intelligent fault diagnosis method based on variational modal decomposition（VMD） improved wavelet denoising and particle swarm optimization（PSO） support vector machine（SVM） is proposed. Firstly， VMD is used to decompose the vibration signal to obtain the intrinsic mode function（IMF） of different scales. The decomposed high frequency component is improved after wavelet de-noising and the low frequency component is reconstructed. Then the feature parameters of reconstructed signal are extracted to construct the feature vector， and kernel principal component analysis（KPCA） is used to realize the feature information fusion. Finally， the PSO optimized SVM is used for fault identification and classification. The experimental results show that the SVM model based on VMD improved wavelet signal preprocessing and PSO algorithm has high recognition accuracy and can effectively and accurately identify and classify the fault types of the crane gearbox.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.018Crane gearboxVariational mode decompositionWavelet decompositionParticle swarm optimizationSupport vector machine |
| spellingShingle | Wubang Yang Bingpeng Gao Fei Chen Xinghe Zhang Weidong Ma Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM Jixie chuandong Crane gearbox Variational mode decomposition Wavelet decomposition Particle swarm optimization Support vector machine |
| title | Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM |
| title_full | Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM |
| title_fullStr | Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM |
| title_full_unstemmed | Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM |
| title_short | Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM |
| title_sort | fault diagnosis of crane gearbox based on variational mode decomposition and pso svm |
| topic | Crane gearbox Variational mode decomposition Wavelet decomposition Particle swarm optimization Support vector machine |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.04.018 |
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