The problem
Why predictive maintenance is hard to get right
Maintenance on a fixed calendar either services equipment that was fine or misses the failure that takes down a line. Unplanned downtime is the expensive outcome, and the warning signs are buried in sensor streams and decades of work orders no one reads. The challenge is predicting failure early enough to act, with enough precision that crews trust the alert instead of ignoring it.
How we build it
01
Condition models from sensor history
Failure and remaining-useful-life models built from your telemetry and maintenance logs, tuned to each asset class.
02
Risk-ranked work orders
Predictions translated into maintenance actions ranked by downtime and safety risk, so crews fix what matters first.
03
Alerting crews trust
Tuned thresholds and clear reason codes that keep false alarms low — because an alarm crews ignore is worse than none.
04
Closed-loop improvement
Feedback from actual outcomes retrains the models, so accuracy compounds instead of decaying after go-live.
The outcome
Unplanned downtime falls and maintenance effort shifts to the assets that actually need it — with alerts precise enough that the floor acts on them.
Related
Key concepts
More use cases