Comprehensive Evaluation of Aero-engine Performance Based on Chaotic Artificial Fish-swarm Optimization Algorithm

Abstract

Targeted at the problem that the performance parameters of aero-engine performance evaluation are difficult to determine, a comprehensive evaluation method of aero-engine performance based on chaotic artificial fish-swarm optimization algorithm is proposed. Based on the actual field guarantee, this paper collects and collates the performance parameter data of aero-engine normal and fault, and obtains the weights of each evaluation index by chaotic search and improved artificial fish swarm optimization algorithm, and compares the chaos search and the improved artificial fish swarm optimization algorithm with the basic artificial fish swarm algorithm and the artificial fish swarm algorithm of the adaptive step and the visual field. The results show that the algorithm based on chaotic artificial fish swarm optimization has fast convergence speed and higher precision. Finally, the model is applied to the case analysis, the performance of multiple engines is queued and the performance trend of single engine is analyzed, and the results show that the performance evaluation is more comprehensive and accurate, which can provide reference for engine performance queue and fault prevention and maintenance plan, and it has important practical significance.