基于量子粒子群和支持向量机的航空发动机性能监控研究

Research of Aero-Engine Performance Monitor Based on Quantum Particle Swarm and Support Vector Machine

摘要: 以量子粒子群算法优化支持向量机中的关键参数(核函数参数σ、不敏感损失系数ε、惩罚系数C)，建立最佳的航空发动机性能监控模型。推力和尾喷口排气温度作为发动机重点监控参数，以两者为基础利用主成分分析的方法建立发动机监控性能综合指标。基于发动机性能模型参数敏感性分析，得到与发动机性能最为密切的参数，作为监控模型输入。通过实例验证，本文建立模型能够很好的监控发动机性能变化，为发动机维修提供参考。

Abstract: The best aeroengine performance monitor model was established by quantum particle swarm algorithm optimizing key parameters (kernel function parameter σ, insensitive loss coefficient ε, punishment coefficient C) of Support Vector Machine. Thrust and outlet exhaust gas temperature were used as key monitor parameter, and synthetic monitor index was established using Principle Component Analysis on the basis of them. The most concerned parameter was used to model input based on analysis of model sensitivity. The example indicates that established model can monitor engine performance, which provided engine maintenance.