Industrial Anomaly
Detection
On-device vibration analysis, acoustic monitoring, and multi-sensor anomaly detection for industrial equipment health monitoring. Predictive maintenance intelligence deployed directly to field hardware — no cloud pipeline required.
Why On-Device Matters Here
Industrial predictive maintenance generates continuous high-frequency data. A MEMS accelerometer sampling at 25.6kHz on a motor bearing produces approximately 200MB of raw data per hour. Transmitting this at scale — across a large motor fleet — is prohibitively expensive and introduces network dependency into a system that must be reliable.
On-device anomaly detection compresses this problem. The edge hardware performs signal processing and inference locally, producing only structured outputs: a health score, an anomaly flag, a maintenance event. The bandwidth requirement drops by orders of magnitude. The system remains operational regardless of network conditions.
WIRL Engineering designs anomaly detection systems for industrial environments — selecting appropriate sensors, designing the signal conditioning and interface hardware, developing the model, and deploying it into firmware that runs reliably in field conditions.
Autoencoder-Based Anomaly Detection
A model trained on known-good operational data learns a compressed representation of normal behavior. Inputs that cannot be reconstructed accurately are flagged as anomalous. This approach requires no labeled fault data to train.
Statistical Feature Extraction + Classifier
Time-domain and frequency-domain features (RMS, kurtosis, crest factor, FFT magnitude bins) extracted from raw sensor data and fed to a compact classifier. Interpretable, computationally efficient, and well-suited for MCU deployment.
Spectrogram CNN
Converting vibration or audio signals to spectrograms and applying lightweight convolutional models. Effective for detecting frequency-domain anomalies that manifest as spectral changes — particularly for rotating machinery.
Threshold-Gated Learning
A lightweight statistical threshold triggers a more sophisticated inference step only when the signal exceeds predefined bounds. This cascade architecture preserves power while maintaining detection sensitivity.
- Sensor selection and signal conditioning design
- MEMS accelerometer and ADC interface engineering
- Signal preprocessing pipeline (filtering, windowing, FFT)
- Feature extraction for time and frequency domains
- Training dataset collection from real equipment
- Anomaly detection model design and training
- Model quantization and MCU deployment
- Threshold calibration and sensitivity tuning
- Alert and event reporting architecture
Predictive Intelligence
in the Field
Describe your equipment and monitoring requirements. We will scope the appropriate sensor and inference architecture.