Sofia Edge Distilled v0.1
A trained 325-parameter NumPy classifier for five synthetic vibration scenarios: healthy, imbalance, misalignment, bearing impulses and rubbing. The teacher is centred kernel ridge regression using a product-rotation kernel with an exact efficient classical evaluation.
Developed by Rootcastle Engineering & Innovation. Source and reproducible training.
Training
14 statistical/spectral features feed a 14 → 16 → 5 ReLU MLP. The teacher uses 600 fit examples and 200 validation examples; its 25-fit scale/penalty search is matched by an RBF control. After selection it is refitted on the 800 training/validation anchors. The student is trained for 120 epochs on 6,000 separately generated waveforms, with 1,000 student-validation examples for checkpoint selection.
The student objective combines hard-label cross-entropy and temperature-2 teacher-target KL. A supervised-only student with identical initialization, data, architecture and optimization budget is retained as a control. Train-only standardization precedes fixed arctangent angle encoding for the teacher.
Held-out results
| Model | Synthetic test accuracy / balanced accuracy |
|---|---|
| Product-kernel teacher | 99.933% |
| Matched-budget RBF teacher | 99.933% |
| Distilled student | 99.933% |
| Supervised-only student | 100.000% |
The balanced test set has 1,500 examples. The distilled student reaches 100.000% on a separately generated shaft-frequency shift of 43–65 Hz, versus 18–42 Hz during training. These results concern easy analytical formulas, not machine diagnosis. The control outperforms distillation on the in-distribution accuracy measure; no distillation quality advantage is claimed.
Use
Download this repository, then run its standalone loader:
import numpy as np
from inference import EdgeModel
model = EdgeModel(".")
signal = np.load("acceleration.npy", allow_pickle=False)
result = model.predict_signal(signal, sample_rate=2048, shaft_hz=25)
print(result)
Acceleration values must be in g. Training windows have 1,024 samples at 2,048 Hz. The loader validates feature order, dimensions, finiteness and the weight SHA-256 before inference.
Artifacts and provenance
model.npz: final float32 student parameters and training-only scaler.config.json: exact ordered feature schema and weight digest.kernel_teacher.npz: anchors, ridge coefficients, centring statistics and selected hyperparameters.dataset.npz: all fit/validation/test feature matrices, labels and held-out scores.metrics.json,protocol.json,training_history.json: measured results and run configuration.verification_report.json: independent kernel and noise control experiments.
The method follows Quantum Artificial Intelligence with Verifiable Kernels. Calculations use classical CPU hardware. Read VERIFICATION.md for exact scope.
Limitations
No measured machine data, field validation or calibrated confidence. Softmax values are scores, not diagnostic certainty. The range check is a feature-distance heuristic. The model cannot validate arbitrary sensor placement, machines or units. It is intended for research and demonstration; it has no machinery actuation API. Only one Edge training seed was run. No quantum processor or quantum computational advantage is claimed.
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