Predictive Maintenance System

Predictive maintenance fails in two familiar ways. Either the sensing is too coarse to carry the signal — low sample rates, missing context, gaps whenever connectivity drops — or the model produces alerts that nobody is accountable for closing. Both end the same way: an expensive dashboard, and maintenance still running to a fixed calendar.

Sensing is designed against the failure modes that matter, not against what is cheapest to install: vibration and current signatures at rates that preserve the diagnostic band, with temperature and process context alongside. An edge node performs windowing and feature extraction locally, so the uplink carries features rather than raw waveforms and a network outage degrades resolution instead of losing the record. Detection runs on residuals against a learned healthy baseline per asset, with drift monitoring on the baseline itself. Every alert opens a work order carrying its evidence, and the closing technician records the observed condition — which is what makes the next model revision possible.