Compressor health monitoring & prescriptive maintenance

The compressor is the most failure-prone and most expensive machine at a hydrogen station — when it trips, the station is offline. Yet maintenance still runs on fixed hour intervals: healthy parts get replaced, worn ones fail before the interval says so.

The warnings exist — bearing wear in vibration spectra, leaking valves in stage temperatures and ultrasound, ring wear in creeping energy consumption — but the signals sit in separate systems and nobody reads them together.

The concept starts with instrumentation: accelerometers on bearings and cylinders, an ultrasonic sensor for valve leakage, per-stage pressures and temperatures, motor power — every channel published over MQTT into a time-series database.

Python analysis turns vibration into spectra where bearing defects, imbalance and misalignment are individually identifiable, scores valves from stage pressure ratios and discharge-temperature deviation, and baselines power per operating point — so drift means wear, not load.

A dashboard condenses everything into per-component health scores and a concrete recommendation: which component, which action, which maintenance window — ranked by failure risk instead of run hours.

Maintenance shifts from fixed intervals to actual condition: work lands in planned downtime, healthy parts stay in the machine.

Faults become diagnosable before the trip — a specific valve or bearing, not a generic vibration alarm.

Status: working prototype on recorded compressor data; ready to pilot on a real machine.