Edge Intelligence
Edge ML Engine
Run anomaly detection, remaining-useful-life prediction, and efficiency optimization directly on the edge device — sub-50ms inference with zero cloud dependency.
Model Library
Three model families purpose-built for rotating and stationary industrial equipment.
Anomaly Detection
Isolation Forest and Autoencoder models flag abnormal vibration, temperature, or pressure patterns before operators notice.
Precision on pump bearing faults
Remaining Useful Life
XGBoost and LSTM models predict days-to-failure for bearings, seals, and impellers using NASA-CMAPSS-trained baselines.
Prediction window accuracy
Efficiency Optimization
Regression models compare real-time BEP deviation against physics curves, recommending VFD setpoints for optimal flow.
Efficiency curve fit
Cloud-to-Edge Pipeline
AutoML pipeline selects features and architecture from operational data
One-click ONNX export with INT8 quantization for edge devices
Staged rollout via OTA — A/B test against production baseline
Engine Specifications
| Runtime | ONNX Runtime 1.17 (CPU) |
| Inference Latency | <50ms per prediction (Pi 4) |
| Batch Throughput | 500+ inferences/sec |
| Model Formats | ONNX, quantized INT8 |
| Max Loaded Models | 20 concurrent models |
| Memory Footprint | <256 MB for 10 models |
| Auto-Update | Cloud-pushed, staged rollout |
| Fallback | Previous model version on failure |
Predict Failures Before They Happen
Deploy ML models to the plant floor in minutes — no data scientist on-site required.