Electrical submersible pump fault diagnosis: A comprehensive review and practical recommendations for real‐time monitoring using an adaptive rule‐based framework

Abstract Electrical submersible pump (ESP) failures often arise from complex interactions among mechanical, electrical, and fluid‐dynamic factors that remain difficult to detect using conventional threshold‐based or black‐box diagnostic models. This comprehensive review examines more than 15 years of ESP diagnostic methods and provides clear practical recommendations for field engineers. This study presents an adaptive hybrid rule‐based expert system for real‐time ESP fault diagnosis, integrating 500 dynamically prioritized rules across 10 functional domains within a feedback‐driven adaptive hybrid inference loop (AHIL). The framework combines deterministic reasoning, statistical drift analysis, and machine‐learning insights to enable contextual interpretation of multi‐sensor data while continuously adjusting rule confidence weights and activation thresholds based on operational feedback. Validation using representative simulated and field‐aligned datasets demonstrates a detection accuracy of 94.3% and an average alert latency of 87 s, corresponding to improvements of approximately 12% and 30%, respectively, compared with conventional supervisory control and data acquisition alarm systems. These results confirm that adaptive rule evolution and interpretability coupling establish a scalable, transparent foundation for intelligent ESP monitoring, bridging the gap between expert‐driven reasoning and data‐driven adaptability.