{"title":"基于Teager-Huang变换的齿轮磨损故障检测与诊断","authors":"Hui Li, Lihui Fu, Zhentao Li","doi":"10.1109/JCAI.2009.11","DOIUrl":null,"url":null,"abstract":"A new approach to fault diagnosis of gear wear based on Teager-Huang transform is presented. This method is based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM)Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for gear fault detection. Teager-Huang transform can effectively diagnose the faults of the gear wear.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"49 21","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fault Detection and Diagnosis of Gear Wear Based on Teager-Huang Transform\",\"authors\":\"Hui Li, Lihui Fu, Zhentao Li\",\"doi\":\"10.1109/JCAI.2009.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new approach to fault diagnosis of gear wear based on Teager-Huang transform is presented. This method is based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM)Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for gear fault detection. Teager-Huang transform can effectively diagnose the faults of the gear wear.\",\"PeriodicalId\":154425,\"journal\":{\"name\":\"2009 International Joint Conference on Artificial Intelligence\",\"volume\":\"49 21\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Joint Conference on Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/JCAI.2009.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Joint Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCAI.2009.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Detection and Diagnosis of Gear Wear Based on Teager-Huang Transform
A new approach to fault diagnosis of gear wear based on Teager-Huang transform is presented. This method is based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM)Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for gear fault detection. Teager-Huang transform can effectively diagnose the faults of the gear wear.