K. Sudheera, N. Nandhitha, N. Ganesh, P. Nanekar, B. Venkatraman, B. Sheela Rani
{"title":"Stockwell变换在超声信号缺陷模式识别中的可行性","authors":"K. Sudheera, N. Nandhitha, N. Ganesh, P. Nanekar, B. Venkatraman, B. Sheela Rani","doi":"10.1109/ICCSP.2014.6949987","DOIUrl":null,"url":null,"abstract":"Ultrasonic Testing is the widely used NDT technique for flaw detection in thick walled weldments. It is an indirect technique and the signals are to be analyzed in order to characterize the flaw. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence the paradigm has shifted to automated signal analysis. In this paper a successful attempt has been made to develop a pattern among the flaws of same type without using Artificial Neural Networks. Here, the signals are analyzed with Stockwell transform and the pattern is determined. Also quantitative characterization is done with mean, standard deviation, root mean square value, peak to rms ratio.","PeriodicalId":149965,"journal":{"name":"2014 International Conference on Communication and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feasibility of Stockwell transform for flaw pattern recognition in ultra sonic signals\",\"authors\":\"K. Sudheera, N. Nandhitha, N. Ganesh, P. Nanekar, B. Venkatraman, B. Sheela Rani\",\"doi\":\"10.1109/ICCSP.2014.6949987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ultrasonic Testing is the widely used NDT technique for flaw detection in thick walled weldments. It is an indirect technique and the signals are to be analyzed in order to characterize the flaw. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence the paradigm has shifted to automated signal analysis. In this paper a successful attempt has been made to develop a pattern among the flaws of same type without using Artificial Neural Networks. Here, the signals are analyzed with Stockwell transform and the pattern is determined. Also quantitative characterization is done with mean, standard deviation, root mean square value, peak to rms ratio.\",\"PeriodicalId\":149965,\"journal\":{\"name\":\"2014 International Conference on Communication and Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Communication and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSP.2014.6949987\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Communication and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSP.2014.6949987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feasibility of Stockwell transform for flaw pattern recognition in ultra sonic signals
Ultrasonic Testing is the widely used NDT technique for flaw detection in thick walled weldments. It is an indirect technique and the signals are to be analyzed in order to characterize the flaw. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence the paradigm has shifted to automated signal analysis. In this paper a successful attempt has been made to develop a pattern among the flaws of same type without using Artificial Neural Networks. Here, the signals are analyzed with Stockwell transform and the pattern is determined. Also quantitative characterization is done with mean, standard deviation, root mean square value, peak to rms ratio.