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 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 dataset. Adam, cross-entropy, a few hundred epochs on CPU in under a minute.

Where it runs

• executorch-zephyr-arduino-uno-q: STM32U585 + Linux side, 13x8 LED matrix chart
• 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

Background: AI for Industrial Control Systems: Bringing Edge AI to PLCs.

License

Apache-2.0.