Enhanced Intelligent Fault Detection of Oil and Gas Field Pipelines Based on Quantum Particle Swarm Optimization and Support Vector Machine

The study proposes an optimisation strategy based on quantum particle swarm optimisation support vector machine for the intelligent detection model of pipeline faults on the surface of oil and gas fields. By combining the global search ability of quantum particle swarm optimisation algorithm and the classification performance of support vector machine, the intelligent classification and feature learning ability of pipeline data are improved. The results show that the proposed algorithm achieves 98.6% in management efficiency, which is 14.7% higher than the traditional particle swarm optimisation support vector machine algorithm, with a cost saving of 14.672% and an assessment accuracy of 93.81%. The strategy effectively improves the efficiency and accuracy of intelligent detection of pipeline faults, which is of great significance in reducing maintenance costs and improving safety management.