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 by 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.