{"title":"Digital filtering for machine tool structural dynamic analysis by the dynamic data system (DDS)","authors":"J.X. Yuan, X.J. Tang , S.M. Wu","doi":"10.1016/0020-7357(86)90005-3","DOIUrl":null,"url":null,"abstract":"<div><p>The Dynamic Data System (DDS) methodology has been successfully used for machine tool structural analysis. In this paper, the digital filtering technique is proposed to reduce the adequate order of the Autoregressive Moving Average Vector (ARMAV) moel to overcome the difficulty in higher order modeling.</p><p>Problems in digital filtering for the ARMAV model are analyzed, including: (1) the selection of an appropriate filter, (2) the elimination of phase distortion, (3) the methods of transformation from an analog filter to discrete filter, and (4) the choice of sampling interval. Two examples are illustrated using the digital filtering technique for identifying the modal and structural parameters of the system.</p></div>","PeriodicalId":100704,"journal":{"name":"International Journal of Machine Tool Design and Research","volume":"26 3","pages":"Pages 267-281"},"PeriodicalIF":0.0000,"publicationDate":"1986-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0020-7357(86)90005-3","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Tool Design and Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0020735786900053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Dynamic Data System (DDS) methodology has been successfully used for machine tool structural analysis. In this paper, the digital filtering technique is proposed to reduce the adequate order of the Autoregressive Moving Average Vector (ARMAV) moel to overcome the difficulty in higher order modeling.
Problems in digital filtering for the ARMAV model are analyzed, including: (1) the selection of an appropriate filter, (2) the elimination of phase distortion, (3) the methods of transformation from an analog filter to discrete filter, and (4) the choice of sampling interval. Two examples are illustrated using the digital filtering technique for identifying the modal and structural parameters of the system.
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