Reactive maintenance is the most expensive kind

The sensors required depend on the failure modes you want to predict. Rotating equipment is typically monitored with vibration accelerometers and bearing/winding temperature sensors. Hydraulic systems use pressure transducers and particle count sensors. Many failure modes can also be detected from existing PLC signals such as cycle time drift without adding new sensors.

For supervised models trained on labelled failure events, 12-24 months of sensor data containing confirmed failure instances is the minimum useful dataset, with at least 5-10 failure events per failure mode. For equipment with rare failures, transfer learning from similar equipment types or unsupervised anomaly detection bridges the data gap.

Unplanned downtime cost depends on your line: lost production per hour plus emergency labour and parts premiums. We model your own number before you commit: we take your current downtime hours per asset, your production cost per hour, and your emergency-versus-planned labour and parts premium, then apply a reduction range derived from how well the models recall your historical failures, net of the inspection cost of false positives. You get a low, base, and high annual saving with a payback estimate. A focused pilot on one equipment class starts at a fixed price; a plant-wide platform is scoped in later phases.

Preventive maintenance services equipment on a fixed calendar, so it replaces parts that still have useful life and still misses failures that happen between intervals. Predictive maintenance watches condition signals and acts only when the data shows degradation. The trade-off to manage is false positives: an alert that sends a technician to healthy equipment costs inspection time, so we tune thresholds against the cost of a missed failure versus the cost of an unnecessary inspection for each asset class.

Fewer vendors explain what must be true about the data: sensor completeness, maintenance-record consistency, operator entry across shifts. Many AI failures are not technology failures; the model works as designed, but the underlying data does not. What must be true inside your organization for this project to succeed: data quality, workforce readiness, governance, integration. Solution selection should work backward from the operational problem, not forward from a vendor feature list.

Factories are not static; degradation happens silently. A production plan must explain monitoring, drift detection, retraining, model versioning, approval, rollback, and production support. A model that cries wolf burns operator trust, so evaluate false alarms and missed events, not headline accuracy.

Strong vendors can clearly describe unsuccessful deployments; weaker vendors claim their solution works everywhere. Ask what percentage of customers have reached production scale, and ask users who did not scale why they pulled the plug. It reveals obstacles vendors rarely highlight in sales. Ask how difficult it is to leave.

A focused pilot covering one equipment class, such as rotating assets on a single line, delivers a validated v1: sensor audit, data pipeline setup, model development, and CMMS integration. That first slice proves the models against your own failure history before you scale. Broader coverage across multiple equipment types and plants grows over later phases rather than shipping all at once.