Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox
Vibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variation...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/871 |
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| author | Zhengxiong Lu Linyue Li Chuanwei Zhang Shuanfeng Zhao Lingxiao Gong |
| author_facet | Zhengxiong Lu Linyue Li Chuanwei Zhang Shuanfeng Zhao Lingxiao Gong |
| author_sort | Zhengxiong Lu |
| collection | DOAJ |
| description | Vibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variational mode decomposition algorithm combined with a genetic algorithm is used to divide the original current signal into several sub-bands with different frequency modulation information, and irrelevant information is preliminarily eliminated. Secondly, an intrinsic mode function (IMF) sub-band fault information extraction method based on lifting wavelet transform is proposed. The minimum entropy value is used to set the sensitive parameters involved in lifting wavelet transform, and the power supply current frequency and noise interference information of a scraper conveyor are removed from the current signal. Finally, it is proved that variational mode decomposition combined with lifting wavelet transform can effectively diagnose the fault of a scraper conveyor by comparative experiments. |
| format | Article |
| id | doaj-art-4b4ae084a1db4d0db28495af0578d1a9 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-4b4ae084a1db4d0db28495af0578d1a92024-12-27T14:37:01ZengMDPI AGMachines2075-17022024-11-01121287110.3390/machines12120871Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor GearboxZhengxiong Lu0Linyue Li1Chuanwei Zhang2Shuanfeng Zhao3Lingxiao Gong4College of Safety Science and Engineering, Xi’an University of Science and Technology, No. 58 Yanta Middle Road, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, No. 58 Yanta Middle Road, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, No. 58 Yanta Middle Road, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, No. 58 Yanta Middle Road, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, No. 58 Yanta Middle Road, Xi’an 710054, ChinaVibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variational mode decomposition algorithm combined with a genetic algorithm is used to divide the original current signal into several sub-bands with different frequency modulation information, and irrelevant information is preliminarily eliminated. Secondly, an intrinsic mode function (IMF) sub-band fault information extraction method based on lifting wavelet transform is proposed. The minimum entropy value is used to set the sensitive parameters involved in lifting wavelet transform, and the power supply current frequency and noise interference information of a scraper conveyor are removed from the current signal. Finally, it is proved that variational mode decomposition combined with lifting wavelet transform can effectively diagnose the fault of a scraper conveyor by comparative experiments.https://www.mdpi.com/2075-1702/12/12/871current signature analysisload impactvariational modal decomposition (VMD)lifting wavelet transform (LWT)feature extraction |
| spellingShingle | Zhengxiong Lu Linyue Li Chuanwei Zhang Shuanfeng Zhao Lingxiao Gong Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox Machines current signature analysis load impact variational modal decomposition (VMD) lifting wavelet transform (LWT) feature extraction |
| title | Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox |
| title_full | Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox |
| title_fullStr | Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox |
| title_full_unstemmed | Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox |
| title_short | Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox |
| title_sort | fault feature extraction based on variational modal decomposition and lifting wavelet transform application in gear of mine scraper conveyor gearbox |
| topic | current signature analysis load impact variational modal decomposition (VMD) lifting wavelet transform (LWT) feature extraction |
| url | https://www.mdpi.com/2075-1702/12/12/871 |
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