How the AI Works
Pipeline Overview
The preprocessing pipeline processes raw CSV telemetry data into clean, overlapping windows for the neural networks.
Input Channels
6 Features
We capture 3 phase currents (Ia, Ib, Ic) directly, and derive 3 phase voltages (va, vb, vc) from the duty cycles and DC bus voltage.
Windowing
200 Samples
Signals are sampled at 5,000 Hz. We extract 40ms windows (200 samples) with a 50% overlap (stride of 100) to ensure continuous monitoring.
Labeling
Fault Detection
Windows before the fault timestamp are strictly labeled "Healthy". Windows after are labeled with the specific fault type. Cross-boundary windows are discarded.
Data Augmentation
To make our models robust to real-world hardware variance, we augment the training data with hardware-realistic noise. We strictly isolate augmentations by split to prevent data leakage.
Techniques
- •Base Augmentations: Gaussian noise (3-5%), gain variation, and linear offset drift.
- •Advanced Probability: Random transient spikes, 3rd/5th harmonic distortions, and slight time warping.
Split Strategy
Model Architectures
MotorShield supports three deep learning models, balancing computational efficiency with long-range receptive fields.
Hybrid CNN–Transformer
~464k Parameters
The ultimate architecture. A CNN stem rapidly downsamples the 200-sample window into 50 rich feature tokens, which are then processed by a 3-layer Transformer Encoder.
Rendering Architecture Diagram...
Improved CNN
~2.1M Parameters
Uses deep residual connections (1D Convs) and channel attention to capture complex local patterns in multi-channel time-series data. It excels at extracting local feature signatures like sudden spikes or phase shifts.
Transformer
~0.4M Parameters
Applies multi-head self-attention directly across the time dimension. While slower on long sequences, it inherently models long-range dependencies and complex, non-periodic transients better than convolutions.
Trace a Prediction
Here's exactly what happens to a 40ms slice of motor data when it hits the Hybrid model.