Assessment and Failure Prediction of Submersible Pumps Using Advanced Modeling Techniques

Abstract

The article examines modern approaches to predictive maintenance of submersible pumps using artificial intelligence algorithms and IoT sensors. The study's relevance is determined by the need to improve the reliability of pump equipment and optimize maintenance costs. It has been established that the main challenges include high infrastructure modernization costs, the complexity of adapting algorithms to variable data streams, and the need for qualified personnel to operate the monitoring system. The study's purpose is to develop recommendations for integrating predictive maintenance systems to reduce unplanned downtime and optimize maintenance costs. The article employs methods of comparative analysis of predictive model efficiency and investigates the impact of key parameters, such as pressure, temperature, and vibration, on real-time equipment condition prediction. The results demonstrated that the proposed model based on recurrent neural networks outperforms traditional approaches across all key metrics, particularly regarding precision and recall. The study concludes that a phased implementation of pilot projects is necessary to adapt the system to industrial conditions and ensure continuous monitoring. The prospects for further research include the development of adaptive models capable of working with incomplete data and enhancing the autonomy of maintenance systems through self-learning algorithms, which will contribute to the stable operation of pump systems under complex operating conditions