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