> ML_RECIPE // INDUSTRIAL-IOT-PREDICTIVE-MAINTENANCE_v1.0
Industrial IoT Predictive Maintenance & Anomaly Detection
Detect early bearing wear and vibration anomalies on factory turbines 72 hours before catastrophic thermal seizure.
Business Outcome
Detect early bearing wear and vibration anomalies on factory turbines 72 hours before catastrophic thermal seizure.
Successfully triggers warning on all historical failure playback datasets.
Heuristic Baseline
Static threshold alarm: Trigger alert when peak vibration amplitude exceeds 4.5 mm/s RMS.
Static vibration threshold produced 14 false alarms per month due to ambient plant resonance.
Phase 1: Prototype Path
Extract frequency-domain vibration telemetry. Fit an Isolation Forest on baseline healthy vibration data to flag outlier energy spikes.
Phase 2: Production Path
Export model to ONNX format. Run sub-millisecond edge scoring on gateway IPC. Stream summary anomalies to factory SCADA and PagerDuty.
Compute & Placement Topologies
Trained quarterly on server CPU using verified healthy operating runs
Edge gateway IPC running ONNX Runtime on industrial low-power CPU
3-Plan Placement Alternatives
Batch anomaly scoring on plant server CPU every 15 minutes via Python scikit-learn daemon.
Industrial edge PC (e.g. Advantech/Siemens DIN-rail) running ONNX Runtime on embedded x86/ARM CPU.
Edge ONNX Runtime gateway with local ring-buffer fallback + centralized MLflow model registry + automated quarterly retraining pipeline on server CPU.
Recommended Libraries & Tools
Governance, Safeguards & Risks
- Never trigger automated mechanical shutdowns solely on ML scores; route through field engineer confirmation.
- Account for factory seasonal ambient temperature shifts in baseline data.