Real-time anomaly detection system for industrial equipment monitoring and early warning of potential system failures
A petrochemical facility needed to monitor thousands of sensors across critical equipment to detect anomalies that could indicate potential failures or safety hazards. Their existing threshold-based alerting system generated too many false alarms while missing subtle patterns that could indicate serious issues, leading to both alert fatigue and missed critical events.
We implemented an unsupervised anomaly detection system using autoencoders and isolation forest algorithms to analyze multi-dimensional sensor data in real-time. The system learned normal operational patterns for each piece of equipment and detected deviations that indicated potential problems, with severity scoring and root cause analysis capabilities.
The anomaly detection system achieved 93% accuracy in identifying genuine equipment anomalies while reducing false positive alerts to just 2.1%. Early detection of equipment issues enabled proactive maintenance, resulting in 38% reduction in maintenance costs and preventing several potential safety incidents. The system processed over 10,000 sensor readings per second with sub-second response times.