A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model

Abstract

Purposes This work has been done in order to overcome the problems of low fault recognition rate, slow convergence speed, and easy falling into local extremum in the current traditional back propagation (BP) model for heat network leakage fault diagnosis. Methods An optimized BP model based on genetic algorithm-ant colony optimization (GA-ACO) algorithm is proposed. The initial value of the pheromone is improved by using the cross-variance operator of the GA algorithm, the iteration speed of the model and the search for the optimal solution are advanced by using the ACO algorithm, the initial weights and thresholds of the BP model are optimized, and the model is applied to the diagnosis of heat network leakage faults through the system simulation software. Results The results show that compared with traditional BP and GA-BP model, the GA-ACO-BP model possesses faster convergence speed and the predicted value is closer to the expected value with less error, which effectively improves the prediction accuracy of heat network leakage faults and enables fast and accurate diagnosis and localization of leakage faults.

Keywords

• heat network leakage
• bp neural network
• genetic algorithm
• ant colony optimization
• fault diagnosis