Predictive maintenance that actually predicted
They had sensors and logs. What they didn’t have was a system that turned that data into “fix this before it fails.”
They had sensors and logs. What they didn’t have was a system that turned that data into “fix this before it fails.”
Maintenance was calendar-based or reactive. When a critical machine went down, the line stopped and everyone scrambled. The team had started collecting vibration, temperature, and run-hour data but didn’t have the analytics or the workflow to act on it. They needed predictions they could trust and a clear handoff to the maintenance crew.
We built a predictive maintenance layer on top of their existing OT and historian data. We modeled failure modes for their most critical assets, trained on historical breakdowns and sensor traces, and deployed inference so that each asset gets a health score and a recommended window for service. Alerts feed into their CMMS and a simple dashboard so planners can schedule work in the next available window instead of after the fact. We started with three high-impact machines and expanded once the team saw the first avoided failure.
Unplanned downtime on the piloted assets dropped. Maintenance shifted from “run to failure” to “fix when the model says so,” and the team gained confidence as predictions matched reality. They’re now rolling the approach to more lines and exploring digital-twin style what-ifs for capacity planning.