Cloud based analysis and prognostics help industrial site monitor over 1000 machines to assist maintenance teams.
Senseye has reached a significant prognostics 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 the future health of over 1,000 machines 24/7, has traditionally been out of reach both technically and financially. We’re proud to have broken a significant milestone in making prognostics accessible to all!” said Robert Russell, CTO of Senseye.
Senseye is a cloud-based condition monitoring and prognostics product and is usable from day one and available as a simple subscription service, enabling customers to rapidly start and expand their predictive maintenance programmes.