Anomaly detection across a pressure-pumping fleet
Real-time anomaly detection on pump fluid ends, scaled across operating locations, with comparative tools that find underperforming pumps early.
Challenge
The customer was spending USD 3 million a month replacing failed pump fluid ends and components, reacting to failures in the field without knowing which pumps were most at risk, and lacking a simple way to compare equipment performance.
Solution
Arundo implemented a set of KPIs that tell operators at a glance about system balance, sensor-signature stability and potential cavitation, applied templated machine learning models to recognise anomalous KPI behaviour, and scaled the solution across the customer's operations with event notifications and investigative tools.
Impact
Operational awareness across the fleet: real-time anomaly detection at every location and early identification of underperforming pumps.