Saar Gillis is a Data Solution Architect @ Cegeka. Working with the Microsoft BI suite since 2000. Saar has been involved in all different aspects of Business Intelligence implementations:
requirements & source analysis, dimensional modeling, database design, ETL development and report development.
Real-time predictive maintenance with Azure ML Studio & PowerBI
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 400