Dynamic predictive maintenance method for oil and gas processing equipment based on long-term prediction model

Abstract

This invention discloses a dynamic predictive maintenance method for oil and gas processing equipment based on a long-term prediction model, particularly suitable for real-time monitoring and fault early warning of oil and gas recovery system equipment status in industrial IoT environments. This method acquires operational data of the oil and gas equipment from sensors and uses this data to train a long-term prediction model, focusing on efficient maintenance of the oil and gas processing equipment. The core of this research lies in the dynamic predictive maintenance strategy, which utilizes multiple long-term prediction models to predict key data of the oil and gas processing equipment, such as pressure and concentration. By analyzing the prediction results and setting reasonable thresholds, potential abnormal trends in the equipment are identified and timely warnings are issued, ultimately generating a predictive maintenance plan and distributing it to the oil and gas processing equipment. Specifically, this research has optimized the best-performing model to better adapt it to the characteristics of oil and gas recovery data. This invention achieves more accurate remote maintenance of oil and gas processing equipment, providing an efficient and low-cost maintenance solution for intelligent management of gas stations, and has significant industrial application value and promising prospects for widespread adoption.

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