Predictive Maintenance of Critical Equipment Using Computer Vision

This presentation will discuss the predictive maintenance of belt conveyors, ladles and furnaces using computer vision and our proprietary artificial intelligence algorithms. Ripik Vision continuously monitors equipment conditions and performance, detecting early signs of wear, defects, misalignment, etc. This proactive approach minimizes unplanned downtime, extends machinery lifespan, and increases overall equipment effectiveness. The presentation will also discuss case studies of how predictive maintenance with Ripik Vision enhanced operational efficiency and reliability by preventing failures and accidents.

Authors:

Mr. Pinak Dattaray | Ripik AI
 

Session Chairs:

John Accurso | Quaker Houghton
 

Cory Langhoff | The Timken Company
 

Chaitanya Sonarikar | ANDRITZ Metals USA Inc.