ArcelorMittal’s rotating assets often operate under harsh conditions. A conveyor at the Ghent hot strip mill facility moves plates of sizzling hot steel along the production process. Under these circumstances, traditional, vibration-based predictive maintenance technologies fail due to high temperatures.
Hear from Carlos Alba (chief digital officer at ArcelorMittal) and Peter D'haese (chief digital officer at ArcelorMittal Flat Europe) on how SAM4 predictive maintenance enables the detection of developing asset faults from inside the motor control cabinet.
12 issues detected
"The first three incidents predicted by Semiotic Labs' SAM4 were not accepted by the maintenance team because they doubted the accuracy of the solution," says D'haese in the video. "Afterwards, those motors also came to failure. And once this conviction was there, every prediction of every failure was accepted by the maintenance team and they replaced the motors quite in advance. So in total, I think we detected 12 issues of which each was confirmed later by a failure in the motor."
To learn more about SAM4 predictive maintenance and how it could help your plant to reduce unplanned downtime, click here to book a demo.