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Real-time predictive maintenance with Azure ML Studio & PowerBI

Speakers: Breght Van Baelen, Saar Gillis

Duration: 1 hour

Maintaining industrial machines is expensive: sensor data needs to be monitored and analyzed, technicians need to be sent out and defects need to be repaired. Predictive maintenance reduces these costs by transitioning from a break-fix model to a prevent-optimize one. To this extend, We propose an end to end Microsoft Azure solution. First, representative sensor data for industrial machines is ingested into Azure with Event Hubs. Using Streaming analytics and Azure ML Studio, we'll predict whether the machine approaches overheating in real-time. If overheating is predicted, the machine will be automatically shut down until cooled again to prevent hardware damage. The maintenance department will be notified by phone or mail and can further analyze the prediction or oppose the automatic shut down in a PowerBI dashboard.

Breght Van Baelen Saar Gillis  400