Equipment health management for wind turbines: 32 channels of vibration/temperature/RPM, CNN diagnosis + VAE anomaly detection + RUL remaining-life prediction, all inferring at the edge — turning reactive repair into predictive maintenance.
The Pain Point: What 'Fix-When-Broken' Costs a Wind Turbine
Turbines run outdoors, at height, under variable loads. Main bearings, gearboxes and generators are the three most fragile assemblies. A single main-bearing seizure means more than a replacement bearing — tower disassembly, heavy transport and lost generation during downtime can push one unplanned stop past a million RMB.
Traditional maintenance has only two modes: scheduled (replace on calendar regardless of actual health, causing over-maintenance) and reactive (fix when broken, the most costly). What the industry lacks is foresight: knowing which unit, which component, and how much life remains — before failure.
Solution: Wind Turbine PHM Predictive Maintenance System
This solution deploys edge intelligence on the turbine — one system covering the full PHM loop of diagnosis + detection + prediction + decision:
- 32-channel real-time acquisition: 32 vibration/temperature/RPM sensors per turbine, 10kHz sampling, capturing the full state of bearings, gearbox and generator
- CNN fault diagnosis: a lightweight 1D-CNN (25KB, 0.3ms) infers at the edge, recognizing known fault modes (inner/outer/rolling-element bearing faults, gear tooth breakage, unbalance, misalignment)
- VAE anomaly detection: a variational auto-encoder trains on normal data only and catches novel anomalies the CNN has never seen — turbine conditions vary too much to pre-label every fault
- RUL remaining-life prediction: TCN temporal model fused with a Wiener physical degradation model outputs '300 days left, 95% confidence 200-450 days' — not just an alarm, but schedulable foresight
- Maintenance decision suggestions: auto-graded by RUL and anomaly score — P0 urgent (stop and inspect now), P1 planned (replace in next maintenance window), P2 normal (continue monitoring)
- Edge deployment: RK3588 inference at the edge, 32 channels in parallel at <5ms each; data never leaves the turbine; monitoring survives network loss
Value: From Reactive Repair to Predictive Maintenance
| Aspect | Traditional | After Deployment |
|---|---|---|
| Strategy | Scheduled replacement, over-maintenance | Health-based scheduling, 20%-30% life extension |
| Fault discovery | After downtime | Alarm at early anomaly stage (weeks ahead) |
| Decision | Experience-based | Quantified RUL + confidence interval + graded advice |
| Spare parts | Hoarded for fear of shortage | Precise RUL-based stocking, lower inventory |
| Data value | Historical data sleeps | Labeled data accumulates, models improve |
Reuse with Light Customization
Built on the standard PHM product with per-model customization: sensor mapping, model fine-tuning (with that model's normal/fault data) and alarm thresholds. Fits direct-drive, doubly-fed and semi-direct-drive turbines, and transfers to motors, pumps, compressors and CNC spindles.
Suitable for: turbine manufacturers (pre-installed, boosting product competitiveness), wind operators (retrofit existing fleets), and any heavy-asset industry dense with rotating equipment.
Let Every Turbine 'Talk'
Turbines sit in the field; discovering failure only after it happens is the costliest path. Predictive maintenance lets a unit 'tell you' weeks ahead what it needs — parts ordered early, windows scheduled early, downtime planned.
Want to know which monitoring approach fits your turbines or rotating assets? Contact us for an assessment.
