Predictive Maintenance β Pocket Perceptron
A binary classifier trained in C++ using the Pocket Perceptron algorithm on the AI4I 2020 Predictive Maintenance Dataset and exported to ONNX.
Model Interface
- Input:
X, FP64 tensor of shape[N, 8] - Output:
Y, INT64 tensor of shape[N, 1] - Labels:
+1= machine failure,-1= normal operation
The eight features are three one-hot encoded machine-type values (L, M, H), followed by air temperature, process temperature, rotational speed, torque, and tool wear.
Numerical features must be standardized using the included scaler.json before inference.
Evaluation
On 2,000 held-out observations:
| Metric | Result |
|---|---|
| Accuracy | 97.15% |
| Precision | 70.37% |
| Recall | 27.94% |
| F1 score | 40.00% |
The model is intended as an educational deployment demonstration, not a production-ready predictive maintenance system.
Deployment
config.pbtxt provides the Datasance ONNX inference engine configuration.
Source project: C++ Pocket PLA β ONNX Runtime β Datasance REST inference.