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license: apache-2.0
library_name: pytorch
pipeline_tag: tabular-classification
tags:
- executorch
- zephyr
- tinyml
- anomaly-detection
- time-series
- industrial
- cortex-m
- int8
datasets:
- eoinedge/heater-anomaly-windows
metrics:
- accuracy
- precision
- recall
model-index:
- name: AnomalyNet
results:
- task:
type: tabular-classification
name: Temperature-window anomaly detection
dataset:
type: eoinedge/heater-anomaly-windows
name: heater-anomaly-windows (test split)
metrics:
- type: accuracy
value: 0.9962
- type: precision
value: 1.0
- type: recall
value: 0.9925
---
# AnomalyNet
An 842-parameter MLP that looks at 32 consecutive temperature samples (3.2 s at 10 Hz) and
says whether the window is normal or contains an anomaly: a spike, a dropout, a runaway
ramp or a burst of noise. It is the "second brain" in my dual-brain PLC demos: ExecuTorch
runs it on the microcontroller next to a ladder-logic scan task or a Linux SoC, and only a
shared variable (`p(anomaly)`) crosses between them.
```
fc1 32->24, ReLU
fc2 24->2 logits [normal, anomaly]
```
Input: `(1, 32)` float, Celsius normalised as `(t - 50) / 20`. Output: 2 logits; softmax
gives `p(anomaly)`.
## Files
| File | What |
|---|---|
| `anomaly_net.py` | model definition and the normalisation constants |
| `anomaly_net.pt` | trained state dict, fp32 |
| `executorch/fp32.pte` | ExecuTorch program, portable fp32 kernels (5.5 KB) |
| `executorch/int8-cmsis-m0plus.pte`, `-m4.pte`, `-m33.pte`, `-m7.pte` | int8 via the ExecuTorch Cortex-M quantizer (3.6 KB) |
| `tflite/fp32.tflite`, `tflite/int8.tflite` | the same weights through a Keras twin for TensorFlow Lite Micro |
| `test_vectors.h` | one normal and one anomaly window with reference logits |
## Results
Held-out synthetic windows (dataset test split, 4,000 windows, 50 % anomalies):
| Metric | Value |
|---|---|
| Accuracy | 99.62 % |
| Precision | 100 % |
| Recall | 99.25 % |
| Parameters | 842 |
| Inference, STM32U585 @ 160 MHz, fp32 | 1.6 ms |
| Inference, nRF52833 @ 64 MHz, fp32 | 4.3 ms |
| Inference, nRF9160 @ 64 MHz, fp32 | 4.0 ms |
## Training
Synthetic data only: the generator in `train/train_anomaly.py` of the
[UNO Q repo](https://github.com/eoinjordan/executorch-zephyr-arduino-uno-q) simulates a
heater loop around 50 C with slow drift and sensor noise, and injects four anomaly types.
The same generator produced the
[heater-anomaly-windows](https://huggingface.co/datasets/eoinedge/heater-anomaly-windows)
dataset. Adam, cross-entropy, a few hundred epochs on CPU in under a minute.
## Where it runs
- [executorch-zephyr-arduino-uno-q](https://github.com/eoinjordan/executorch-zephyr-arduino-uno-q): STM32U585 + Linux side, 13x8 LED matrix chart
- [executorch-zephyr-arduino-opta](https://github.com/eoinjordan/executorch-zephyr-arduino-opta): beside a ladder-logic scan task on a PLC
- micro:bit v2, Thingy:91, LAUNCHXL-CC1352P1 self-tests, and the
[ExecuTorch vs TFLM benchmarks](https://github.com/eoinjordan/executorch-vs-tflm-benchmarks)
Background: [AI for Industrial Control Systems: Bringing Edge AI to PLCs](https://www.edgeimpulse.com/blog/transform-industrial-control-systems-with-ai-anomaly-detection-made-easy/).
## License
Apache-2.0.
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