{"title":"Application of HSMM on NC machine's state recognition","authors":"H. Qiang, Ding Zhihua, Zhang Xiao","doi":"10.1109/EDT.2010.5496609","DOIUrl":null,"url":null,"abstract":"It is significant to identify the running-states of NC machines for ensuring the machining accuracy and running stability. Vibration diagnosis is an on-line prognostics and diagnosis technique by picking-up the frequency characters of the vibration signal on NC machine. In the paper, combining with the wavelet noise reduction and character extraction with varying scales, the Hidden Semi-Markov model is built by the example of headstock bearing abrasion to recognize the running-states effectively. According to experiment and simulation researches, it indicates that the veracity of identification is 96.7% in the 120 test samples after training the HSMM with 80 training samples. This fault diagnosis method is satisfied for the engineering demand, and it can be applied for vibration analysis for other complex machineries.","PeriodicalId":325767,"journal":{"name":"2010 International Conference on E-Health Networking Digital Ecosystems and Technologies (EDT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on E-Health Networking Digital Ecosystems and Technologies (EDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDT.2010.5496609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
It is significant to identify the running-states of NC machines for ensuring the machining accuracy and running stability. Vibration diagnosis is an on-line prognostics and diagnosis technique by picking-up the frequency characters of the vibration signal on NC machine. In the paper, combining with the wavelet noise reduction and character extraction with varying scales, the Hidden Semi-Markov model is built by the example of headstock bearing abrasion to recognize the running-states effectively. According to experiment and simulation researches, it indicates that the veracity of identification is 96.7% in the 120 test samples after training the HSMM with 80 training samples. This fault diagnosis method is satisfied for the engineering demand, and it can be applied for vibration analysis for other complex machineries.
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