Algorithm for Fault Diagnosis System of Pumping Units Based on Fuzzy Logic and Neural Networks

In order to reduce the exploitation cost, improve the economic benefit and ensure the sustainable exploitation of crude oil, it is particularly urgent to innovate the oil extraction technology. This paper introduces two advanced intelligent technologies, fuzzy logic and neural network. Fuzzy logic can deal with uncertain and fuzzy information, which provides an effective means to deal with the complex faults of pumping units. Neural network, through its powerful self-learning and pattern recognition ability, provides a high-accuracy solution for fault classification and recognition of pumping units. Through experimental verification, this paper finds that the fault diagnosis method of pumping unit based on fuzzy logic and neural network is significantly better than the traditional method in accuracy and efficiency. This method can not only identify fault types and causes more accurately, but also realize timely early warning and prediction of faults, thus greatly improving the safety and production efficiency of oil fields. The fault diagnosis technology based on fuzzy logic and neural network provides strong support for the intelligent development of oil fields, and also lays a solid foundation for the intelligent, efficient and safe exploitation of oil fields in the future.