Overview
Pipeline leaks and ruptures can lead to catastrophic environmental damage, regulatory penalties, and severe reputational loss. Traditional SCADA-based monitoring systems often detect issues too late or miss slow-developing leaks altogether. Using real-time data from flow and pressure sensors via AWS IoT Core and Kinesis, we deploy statistical and ML-based anomaly detection models with Amazon Lookout for Metrics. These models are calibrated to detect early leak signals and integrated with AWS IoT Events to trigger alerts and automated responses. The result is faster detection, containment, and reduced environmental impact.
Our Solution:
Stream sensor data (pressure, flow, acoustic signals) via low-latency pipelines. Use anomaly detection models to flag early signs of leakage or structural issues. Deliver event-driven alerting and map-based visual monitoring to control rooms.
Underlying AWS stack:
Amazon Kinesis Data Analytics – Real-time analysis of pipeline sensor data. Amazon Lookout for Metrics – Detect statistical anomalies in flow/pressure. AWS IoT Events – Define and respond to pipeline events. AWS Lambda – Trigger responses (e.g., shutoffs, alerts). Amazon CloudWatch Dashboards – Visualize operational pipeline metrics.
Highlights
- Early Leak Detection and Rapid Response: Identify anomalies before they escalate using real-time ML models and trigger automated shutoffs or alerts to prevent environmental harm.
- Reduced Environmental and Regulatory Risk: Minimize the impact of leaks with faster containment and ensure compliance with safety standards.
- Integrated, Scalable Monitoring: Leverage AWS tools for continuous, low-latency data streaming and visualization across the entire pipeline network.
Details
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