Mohamed Khalil, C. Bergs, T. Papadopoulos, R. Wüchner, K. Bletzinger, M. Heizmann
{"title":"IIoT-based Fatigue Life Indication using Augmented Reality","authors":"Mohamed Khalil, C. Bergs, T. Papadopoulos, R. Wüchner, K. Bletzinger, M. Heizmann","doi":"10.1109/INDIN41052.2019.8972114","DOIUrl":null,"url":null,"abstract":"Online condition monitoring services and predictive maintenance are becoming more and more a key for system operators to extend the system lifetime and detect faults in early stages. Therefore, system manufactures need to efficiently provide system operators so-called digital twins which can be executed during operation and give the system operator an impression of the health state of the system. Industrial Internet of Things (IIoT) platforms are enablers for such services and provide new possibilities to interact with the system running in the field. Furthermore, the traditional dashboard are becoming obsolete as user interface and are replaced by novel solutions that let the system operator experience the system health state. For example, health estimation and condition monitoring of electric motors is a topic of high interest nowadays. This article addresses an application which acquires machine data, processes it on an IIoT platform to get the system health and visualizes the results online in an augmented reality user interface.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN41052.2019.8972114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Online condition monitoring services and predictive maintenance are becoming more and more a key for system operators to extend the system lifetime and detect faults in early stages. Therefore, system manufactures need to efficiently provide system operators so-called digital twins which can be executed during operation and give the system operator an impression of the health state of the system. Industrial Internet of Things (IIoT) platforms are enablers for such services and provide new possibilities to interact with the system running in the field. Furthermore, the traditional dashboard are becoming obsolete as user interface and are replaced by novel solutions that let the system operator experience the system health state. For example, health estimation and condition monitoring of electric motors is a topic of high interest nowadays. This article addresses an application which acquires machine data, processes it on an IIoT platform to get the system health and visualizes the results online in an augmented reality user interface.