Project background
Unplanned downtime on industrial machines is expensive and often preventable. The client wanted a practical predictive-maintenance system that produced lead time rather than hindsight.
Challenge
Different machines have different failure modes; a one-size-fits-all model won't work. Building a framework that can be calibrated per asset class without needing a data scientist per deployment.
Approach & solution
We instrumented machines with vibration, temperature, and acoustic sensors, then built a per-asset-class modeling pipeline that self-learns baseline behavior and flags deviations. Maintenance teams receive alerts with recommended inspection steps and a confidence score.
Results & benefits
Early warnings now arrive days or weeks ahead of failures in pilot assets, giving maintenance teams time to schedule interventions. Unplanned downtime dropped noticeably across the initial fleet.
