A field-ready guide to predictive maintenance in manufacturing — sensors, edge inference, alerting policy, and integrating with the CMMS.
Binate AI
July 10, 2025
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PdM wins on assets with high cost of unplanned downtime, available sensor data, and stable operating conditions. Conveyor belts, motors, pumps, and presses are good candidates. Highly variable processes are harder.
Sub-second inference belongs at the edge; aggregation and retraining belong in the cloud. The two halves talk through a lightweight broker.
Industrial PC or Jetson at the asset. Runs the model. Decides locally on the high-frequency signals.
MQTT or Kafka. Pushes summaries and exceptions to the cloud.
Maintains long-window features (30d, 90d). Catches slow drifts the edge misses.
Weekly or monthly. Validates new model on a held-out set before promotion.
Alerts open work orders in the maintenance system. Closes the loop on actions taken.
A perfectly accurate model with a noisy alerting policy is ignored within a quarter. Tune for technician trust.
Bad alerts
Good alerts
24–72h
Typical advance warning
30–45%
Unplanned downtime reduction
15–25%
Maintenance cost reduction
We deploy edge + cloud PdM for manufacturing plants in the US.
See our manufacturing workPdM wins on architecture and alerting policy, not just on model accuracy. Buy the lead time, integrate with the CMMS, and respect technician trust.
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