Predictive maintenance is one of the most-pitched, most-pilot, and least-deployed AI applications in industrial settings. Three patterns separate production deployments from pilots-that-stall.
Generic predictive-maintenance models don’t work. Each asset class (rotating equipment, fixed equipment, fluid systems) needs its own model architecture. Vibration analysis on bearings ≠ thermal analysis on transformers.
A prediction is worthless without an action. The model output must trigger a maintenance work order with parts, technician, and timing — automatically. Models that generate alerts maintenance teams ignore are demoware.
Equipment degrades over time. Operating conditions change. Models drift. Continuous retraining triggered by operational feedback is non-negotiable.
Without all three, you have a dashboard. With all three, you have predictive maintenance.
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