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 commonly costs $5,000 to $50,000 per hour in lost production on a mid-market line, and far more on a high-throughput plant. Deloitte reports that mature predictive maintenance cuts equipment breakdowns by around 70% and maintenance costs by about 25%. 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, usually 6 to 18 months. A focused pilot on one equipment class starts around $30,000 to $60,000 at a fixed price; a plant-wide platform grows into six figures over 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.

A focused pilot covering one equipment class, such as rotating assets on a single line, delivers a validated v1 in 10 to 14 weeks: 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.