A Rule-Based Expert System for Real-Time Fault Diagnosis in Electrical Submersible Pump Systems

Abstract Electrical Submersible Pumps are critical components in oil production systems but are prone to failures due to harsh operating conditions. This paper presents a comprehensive rule-based expert system designed for real-time fault diagnosis in ESP systems. The proposed framework integrates 500 diagnostic rules across ten functional categories, including motor current, temperature, pressure, vibration, voltage, VFD current, derived parameters, statistical anomaly detection, machine learning insights, and field experience. These rules follow an interpretable IF–THEN–ACTION logic, enabling operators to understand the reasoning behind each alert and take timely corrective actions.The system is validated through a representative dataset simulating real-world ESP operational scenarios. Results demonstrate a detection accuracy of 94.3%, with alerts generated within an average of 87 seconds, ensuring early fault identification and minimizing unplanned downtime. The system also supports integration with SCADA platforms for centralized monitoring and automated intervention.By combining domain expertise with multi-sensor data and statistical analysis, this work bridges the gap between traditional manual interpretation and modern data-driven diagnostics. The rule-based expert system offers a practical, scalable, and transparent solution for enhancing ESP reliability and optimizing production operations.