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Article

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
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Appl. Sci. 2020, 10(7), 2377; https://doi.org/10.3390/app10072377
Received: 25 February 2020 / Revised: 25 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out. View Full-Text
Keywords: digital twin; decision support system; factor analysis; KPI; quantitative analysis; root-cause analysis digital twin; decision support system; factor analysis; KPI; quantitative analysis; root-cause analysis
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MDPI and ACS Style

Papacharalampopoulos, A.; Giannoulis, C.; Stavropoulos, P.; Mourtzis, D. A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT. Appl. Sci. 2020, 10, 2377. https://doi.org/10.3390/app10072377

AMA Style

Papacharalampopoulos A, Giannoulis C, Stavropoulos P, Mourtzis D. A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT. Applied Sciences. 2020; 10(7):2377. https://doi.org/10.3390/app10072377

Chicago/Turabian Style

Papacharalampopoulos, Alexios, Christos Giannoulis, Panos Stavropoulos, and Dimitris Mourtzis. 2020. "A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT" Applied Sciences 10, no. 7: 2377. https://doi.org/10.3390/app10072377

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