Performance optimization and prediction of "ESP" Pump using artificial intelligence

Abstract

Predictive maintenance aims to anticipate potential equipment failures before they occur, allowing for the implementation of well-planned preventive measures that reduce unplanned downtime and high maintenance costs. Among the most effective and widely used methods in this field is the analysis of vibration data, which enables early detection of mechanical anomalies in industrial systems.In this context, the present study focuses on the application of artificial intelligence techniques, particularly the Random Forest and Artificial Neural Networks (ANNs) algorithms, to develop an intelligent model for monitoring and diagnosing faults in an Electric Submersible Pump (ESP) system. The integration of machine learning algorithms into vibration signal analysis has proven effective in enhancing diagnostic accuracy compared to traditional methods based on time-domain and frequency domain analysis. The models were trained on real operational data to recognize abnormal vibration patterns and to identify the main factors contributing to system shutdowns. The primary objective of this work is to improve fault prediction accuracy and to provide early alerts that enable the implementation of efficient predictive maintenance strategies. A comparative analysis between the two models was conducted to evaluate their performance and determine the most accurate and reliable approach for real-time fault classification.