L. Cristaldi, Massimo Lazzaroni, A. Monti, F. Ponci
{"title":"A neuro-fuzzy application for AC motor drives monitoring system","authors":"L. Cristaldi, Massimo Lazzaroni, A. Monti, F. Ponci","doi":"10.1109/IMTC.2003.1208025","DOIUrl":null,"url":null,"abstract":"Nowadays industrial applications require suitable monitoring systems able to identify any decrement in the efficiency involving economical losses. This paper shows that the information coming from a general purpose monitoring system can be usefully exploited to realize a sensorless instrument able to monitor an ac motor drive and diagnostic tools providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a Virtual Instrument. The employed wavelet algorithms have been implemented in a Matlab environment and risk coefficients are elaborated by means of suitable neuro fuzzy algorithms.","PeriodicalId":135321,"journal":{"name":"Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMTC.2003.1208025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
Nowadays industrial applications require suitable monitoring systems able to identify any decrement in the efficiency involving economical losses. This paper shows that the information coming from a general purpose monitoring system can be usefully exploited to realize a sensorless instrument able to monitor an ac motor drive and diagnostic tools providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a Virtual Instrument. The employed wavelet algorithms have been implemented in a Matlab environment and risk coefficients are elaborated by means of suitable neuro fuzzy algorithms.