D. Kouril, Jakub Bača, M. Sobek, M. Kuchar, Jan Strossa
{"title":"Data Acquisition System for the Modern Induction Motor Drive applications","authors":"D. Kouril, Jakub Bača, M. Sobek, M. Kuchar, Jan Strossa","doi":"10.1109/EPE51172.2020.9269184","DOIUrl":null,"url":null,"abstract":"This paper describes a hardware and software solution for data acquisition [1]–[4] for implementation in the field of modern sensorless control methods of induction motor drive [5], where especially adaptive control methods based on Artificial Neural Network (ANN) require a large amount of training data for their operation. Also in industrial applications nowadays, diagnostic systems are often used. These systems work on the principle of data acquisition and enable detection of unwanted faults during stable operation of automated technologies and data collection of quantities that are accessible to superior systems and seemingly unrelated to a motor drive control, such as information about the temperatures and mechanical state. In this way, for example, it is possible to overall increase productivity and safety over the lifetime of the automated technology. This article is divided into several parts. In the first part, based on the requirements, analysis, and design of a suitable solution for data acquisition and advantages over other solutions is performed. The second part of the paper deals with the hardware design of the data acquisition system solution. The third part is focused on the software design and implementation in the LabVIEW and MATLAB environment. The last part of the article is devoted to the analysis of experimental results.","PeriodicalId":177031,"journal":{"name":"2020 21st International Scientific Conference on Electric Power Engineering (EPE)","volume":"31 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 21st International Scientific Conference on Electric Power Engineering (EPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPE51172.2020.9269184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper describes a hardware and software solution for data acquisition [1]–[4] for implementation in the field of modern sensorless control methods of induction motor drive [5], where especially adaptive control methods based on Artificial Neural Network (ANN) require a large amount of training data for their operation. Also in industrial applications nowadays, diagnostic systems are often used. These systems work on the principle of data acquisition and enable detection of unwanted faults during stable operation of automated technologies and data collection of quantities that are accessible to superior systems and seemingly unrelated to a motor drive control, such as information about the temperatures and mechanical state. In this way, for example, it is possible to overall increase productivity and safety over the lifetime of the automated technology. This article is divided into several parts. In the first part, based on the requirements, analysis, and design of a suitable solution for data acquisition and advantages over other solutions is performed. The second part of the paper deals with the hardware design of the data acquisition system solution. The third part is focused on the software design and implementation in the LabVIEW and MATLAB environment. The last part of the article is devoted to the analysis of experimental results.