Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM
The characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2023-02-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.02.021 |
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| _version_ | 1841547034100760576 |
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| author | Zhao Xiaohui Tan Qi Hu Sheng Yang Wenbin Huan Kaixuan Zhang Zhijie |
| author_facet | Zhao Xiaohui Tan Qi Hu Sheng Yang Wenbin Huan Kaixuan Zhang Zhijie |
| author_sort | Zhao Xiaohui |
| collection | DOAJ |
| description | The characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. Firstly, the fault vibration signal is decomposed by LMD to obtain several PF components, and the PF components with higher correlation are screened out using the correlation coefficient principle. Secondly, the screened PF components are input into the cloud model, and the feature vectors are extracted using the inverse cloud generator and input into PSO-KELM for fault diagnosis. Finally, the performance of the method is analyzed using the measured data of the QPZZ-Ⅱ test-bed gearbox. The results show that the recognition accuracy of the method is 97.65%, and compared with various methods this method has the best recognition performance. |
| format | Article |
| id | doaj-art-6e62d9fac73245758dfe4c81e3e49e2a |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-6e62d9fac73245758dfe4c81e3e49e2a2025-01-10T14:57:04ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392023-02-014715716334892119Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELMZhao XiaohuiTan QiHu ShengYang WenbinHuan KaixuanZhang ZhijieThe characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. Firstly, the fault vibration signal is decomposed by LMD to obtain several PF components, and the PF components with higher correlation are screened out using the correlation coefficient principle. Secondly, the screened PF components are input into the cloud model, and the feature vectors are extracted using the inverse cloud generator and input into PSO-KELM for fault diagnosis. Finally, the performance of the method is analyzed using the measured data of the QPZZ-Ⅱ test-bed gearbox. The results show that the recognition accuracy of the method is 97.65%, and compared with various methods this method has the best recognition performance.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.02.021GearboxFault diagnosisLocal mean decompositionCloud modelParticle swarm optimization kernel extreme learning machine |
| spellingShingle | Zhao Xiaohui Tan Qi Hu Sheng Yang Wenbin Huan Kaixuan Zhang Zhijie Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM Jixie chuandong Gearbox Fault diagnosis Local mean decomposition Cloud model Particle swarm optimization kernel extreme learning machine |
| title | Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM |
| title_full | Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM |
| title_fullStr | Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM |
| title_full_unstemmed | Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM |
| title_short | Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM |
| title_sort | gearbox fault diagnosis based on the lmd cloud model and pso kelm |
| topic | Gearbox Fault diagnosis Local mean decomposition Cloud model Particle swarm optimization kernel extreme learning machine |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.02.021 |
| work_keys_str_mv | AT zhaoxiaohui gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm AT tanqi gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm AT husheng gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm AT yangwenbin gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm AT huankaixuan gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm AT zhangzhijie gearboxfaultdiagnosisbasedonthelmdcloudmodelandpsokelm |