Machine-learning models trained on historical sensor data power a real-time monitoring system that watches thousands of telemetry points per second. It surfaces irregular flow patterns and equipment degradation before they turn into a line stoppage.
The challenge
Massive volumes of streaming sensor data with varying quality and reliability. Baseline patterns that differ across flowmeter types and operating conditions. The need to minimise false positives while holding detection sensitivity high. And to integrate with the legacy SCADA systems already running at client facilities, without a rip-and-replace.
The approach
A hybrid of statistical process control and deep-learning autoencoders. An ensemble of models, each specialised for point, contextual or collective anomalies, sits on a feature pipeline that extracts temporal patterns at multiple time scales, telling normal operational variation apart from a genuine fault.
The discipline that kept crews trusting the system was the false-positive rate: under two percent.
The results
The deployed system achieved a 95% detection rate for critical anomalies at under 2% false positives.
- 40%
- Less unplanned downtime
- 95%
- Anomaly detection rate
- 25%
- Lower maintenance cost
Early detection cut unplanned downtime by 40% and maintenance costs by 25%. Every monitored facility moved from reactive firefighting to proactive, condition-based maintenance.