The Pipeline That Predicts Failure
a North American pipeline operator
saved annually
fewer unplanned shutdowns
average early warning before failure
Unplanned shutdowns cost millions per incident, yet maintenance was driven by fixed schedules rather than actual asset condition. Sensor data streamed in from thousands of miles of pipeline but sat unused, and the knowledge needed to interpret anomalies lived in decades of maintenance manuals and the heads of retiring engineers. The operator needed to catch failures before they happened, not respond after.
How we approached it
Consolidated sensor telemetry, inspection records, and failure history into a single asset data foundation.
Trained time-series anomaly models on pressure, flow, and vibration data to flag degradation weeks before failure thresholds.
Built a RAG system over maintenance manuals, inspection reports, and past work orders so field engineers get sourced repair guidance for each alert.
Wired predictions into the work order system so alerts arrive prioritized by failure risk and cost of downtime.
“We used to find out about problems when the line went down. Now we get three weeks of warning and a repair plan pulled from our own manuals.”
Savings combine avoided shutdown incidents against the operator's historical baseline with reduced emergency repair premiums, validated by the finance team over the first full year.
Pressure, flow, and vibration telemetry from existing pipeline sensors, combined with inspection records and failure history, so no new instrumentation was required to start.
Each alert links to relevant manual sections, past work orders, and inspection reports with citations, so engineers arrive on site with a sourced repair plan instead of starting from scratch.
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