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Senseye, the Uptime as a Service leader today announced that it has reached a significant prognostics-at-scale milestone of automatically monitoring over 1,000 machines at a single customer site for early signs of mechanical damage and failure; using machine learning to help the client to avoid unplanned downtime, without relying on expensive consultants.

Senseye is based in the cloud and has been able to rapidly scale with client demands after quickly proving its capabilities on a small cluster of machines. Rather than relying on preventative maintenance, the client is now automatically notified of current issues as well as the Remaining Useful Life of industrial machinery, enabling its maintenance teams to implement effective predictive maintenance.

“Senseye is designed to make condition monitoring and prognostics analysis at scale accessible and affordable. Automatically monitoring...

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