Data collection & cleansing
Collect sensor, PLC and inspection-equipment data, then remove noise and outliers according to operating conditions.
Algorithm Development
Algorithm Development
On-device AI diagnostic algorithms that read equipment condition from sensor data
NeuroSense collects and cleans field sensor and equipment data, then develops anomaly-detection and condition-diagnostic algorithms using time-series feature extraction such as FFT, STFT, order analysis and envelope analysis. We protect implementation details while showing the public-facing capability: data pipeline, model development and app integration delivered as one flow.
Discuss algorithm developmentDevelopment Flow
Feature extraction
Extract useful frequency- and time-domain features from vibration, current, temperature and other time-series data.
AI diagnostic model
Build models that learn healthy baselines and classify or diagnose early anomaly signals for each site.
Edge embedding
When needed, deploy algorithms to sensors, gateways or on-site Edge servers for real-time decisions.
Application Areas
Sensitive details such as customer names, defense configurations and proprietary implementation are excluded from the public screen; the page focuses on capabilities that can be shared externally.
- Rotating-equipment vibration anomaly detection
- Fastening-curve quality diagnostics
- Pipeline impact and leak-event detection
- Fuel-silo blockage early warning
- Vehicle NVH and drivetrain condition diagnostics
- PHM and CBM+ decision support