We learned if we could predict events at least 30 mins to 1 hour before the occurrence, our client’s Ops/Facilities team can take appropriate actions to prevent vital asset failures.

Using Predictive Maintenance and Industrial AI, we developed a solution that would identify failure and reliability issues before they occurred. By collecting operational data, real-time field data and historical downtime data, we generated predictive models that tested and validated our dataset again and again, each time learning and improving from previous successes.

The end result was an algorithm that assigned a value between 0 and 1 to indicate the risk of the compressor going down in the next 30 minutes, delivering real-time insights on asset performance.