AI & Analytics
Anomaly Detection
Multi-algorithm anomaly detection that catches equipment issues hours before traditional threshold alarms. Five algorithms running in consensus to minimize false positives.
Detection Algorithms
Five algorithms running in parallel, each with different strengths.
| Algorithm | Type | Strength | Latency |
|---|---|---|---|
| Isolation Forest | Unsupervised | Catches multi-dimensional outliers across correlated sensor groups | <50ms |
| Autoencoder | Deep Learning | Learns complex normal operating patterns; detects subtle deviations | <80ms |
| Statistical Process Control | Statistical | CUSUM and EWMA charts for gradual drift detection over time | <5ms |
| One-Class SVM | Semi-Supervised | Effective with limited training data; good for rare equipment types | <30ms |
| Spectral Residual | Frequency Domain | Detects periodic anomalies and unexpected frequency components | <20ms |
Detection Timeline
Core Capabilities
Hours of Early Warning
Detect anomalies 2-8 hours before traditional threshold alarms fire. Enough lead time to plan a response.
Cross-Sensor Correlation
Analyze relationships between vibration, temperature, pressure, and flow simultaneously. Catch issues no single sensor reveals.
Adaptive Baselines
Models retrain continuously on recent data. Seasonal changes, load variations, and process shifts are learned automatically.
Consensus Scoring
Multiple algorithms must agree before an anomaly is confirmed. Reduces false positives by 85% compared to single-model approaches.
Detect Problems Before They Become Failures
Hours of early warning instead of seconds. Multi-algorithm consensus instead of single-threshold guessing.