On-Demand Webinar: Canvass Catalyst: Predictive Maintenance AI models for Gearbox Failure Prediction
Early detection of failures in rotating machinery can significantly reduce the costs of maintenance and prevent total failures for oil and gas and mining companies. AI predictive maintenance models can ultimately increase the operational safety of gearboxes. Learn how leading Oil & Gas and Mining companies are using the Canvass platform's ready-to-use templates to build explainable AI models to predict gearbox failures. This webinar will showcase the advantages of using AI predictive maintenance models over point-based anomaly detection models to deliver the predictive insights operators need to enhance their maintenance activities and reduce unplanned downtime due to gearbox failures.
The Canvass Catalyst Series focus on real-world examples of industrial AI in action. In this series, learn how Industrial AI can improve your operations, customer case studies, and best practice advice for a successful AI journey.
About the presenter:
Eman Nejad | Chief Data Scientist
Eman Nejad is Canvass AI’s Chief Data Scientist. Eman brings an extensive background in building end-to-end AI pipelines for industrial challenges. His experience in developing data-driven applications is supporting leading companies in the oil and gas, food and agriculture, metals and mining, energy and utilities, and chemicals industries to achieve their goals using the Canvass AI platform. He holds Ph.D. in Electrical and Computer Engineering from the University of Windsor where he focused on developing AI models for computer vision problems.
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