Emilia Romagna
Predictive Maintenance of a conveyor belt through a Digital Twin driven by ML
(Domenico Guida @ ART-ER, Francesco Mambello @LIAMLAB, Gianfraanco Modoni @STIIMA-CNR)
NEED for AI:
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The objective of the experiment is to monitor the state of a curved conveyor belt in order to predict potential faults. In this regard, AI-related technologies provide a valid backbone to realize a prediction model whose potential is investigated in this experiment.
AI REGIO SOLUTION:
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The solution is based on the Digital Twin For Predictive Maintenance, which is included in the AI REGIO assets catalog and also in the AI4EU portal.
The solution analyzes the data collected by various sensors installed in fixed points along the curve. In addition, for the analysis, the model of Digital Twin includes a data analytics module that leverages Machine Learning techniques.
EXPECTED BENEFITS:
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Improving the level of production by improving the machine quality consistency
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Scheduling interventions at the optimal time
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Optimizing the production time and the personnel efforts
The EMILIA ROMAGNA region DIH experiment is focused on
the demonstration of the use of AI-related technologies such Digital Twin and Machine Learning to realize the Predictive Maintenance for a conveyor belt.