Predictive maintenance that prevents costly downtime
We built a real-time anomaly detection system that monitors sensor data from 200+ machines, predicting equipment failures 48 hours before they happen.
Where they started.
ProManufact runs 3 assembly plants with 200+ CNC machines, conveyors, and quality inspection systems. Unplanned downtime cost them $50K per hour. Reactive maintenance meant failures happened without warning; preventive maintenance was calendar-based and often unnecessary. They had sensors everywhere, but the data sat unused.
How we solved it.
We consolidated sensor data from their SCADA systems, machine PLCs, and quality gates into a unified time-series pipeline. We addressed sampling rates, alignment, and missing data.
We built anomaly detection models per machine type: autoencoders for baseline behavior, with alerts when the reconstruction error exceeded thresholds. We tuned sensitivity to minimize false positives while catching real failures.
We developed a "time to failure" prediction layer: given current sensor patterns, the model estimates hours until likely failure. We targeted 48-hour early warning for critical equipment.
We integrated with their maintenance scheduling system: predicted failures generate work orders with priority and recommended actions.
We deployed a dashboard for plant managers showing machine health scores, predicted failures, and recommended maintenance windows.
The outcome.
35% reduction in unplanned downtime within the first year of deployment.
48-hour early warning for 90% of critical equipment failures, enough time to schedule maintenance without disrupting production.
Maintenance costs dropped 20% as calendar-based preventive maintenance was replaced with condition-based scheduling.
The system has been expanded to two additional plants with consistent results.
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