Data-driven Anomaly Detection Model for The Fluid End Subsystem in Cementing Equipment

The fluid end subsystem in cementing equipment is critical to the reliability and performance of pressure pumping operations. Failures in components such as valves, plungers, and seals can lead to costly downtime and operational risks. This paper presents a lightweight and interpretable anomaly detection method for identifying fluid end anomalies. The approach leverages the deviation between theoretical flow rate calculated from crankshaft speed and actual flow rate measured by a flowmeter to compute a normalized flow rate difference (NFRD). A health indicator is extracted from NFRD per cementing job, and anomalies are detected using a threshold-based classifier. Validation on three cementing pumping units demonstrates the method’s effectiveness, achieving a 66.7% detection rate and 75% precision. The proposed model supports rapid troubleshooting, enhances maintenance planning, and reduces unplanned downtime.