应用支持向量回归机探索发动机VSV调节规律

发动机可调静子叶片（VSV）调节规律极其复杂，通过挖掘快速存取记录装置（QAR）数据对VSV调节规律进行了深入研究。首先，通过PW4077D发动机健康状态的QAR数据，建立基于粒子群优化（PSO）算法的支持向量回归机（SVR）模型，来探索VSV调节规律；然后，利用后续航班数据对PSO-SVR模型进行验证，并将验证结果与传统的PSO-BP神经网络模型进行对比；最后，应用PSO-SVR模型进行发动机故障诊断。研究结果表明:PSO-SVR模型的回归预测精度优于PSO-BP神经网络模型，能够准确反映VSV的调节规律。可将其用于发动机的状态监控和故障诊断，亦可为VSV控制系统设计提供参考。 

Exploration of engine VSV regulation law using support vector regression

The engine variable stator vane (VSV) regulation law is extremely complex, and through mining quick access recorder (QAR) data, the VSV regulation law is studied. Firstly, the support vector regre-ssion (SVR) model based on particle swarm optimization (PSO) is established through the QAR data of PW4077D engine health condition to explore the regulation law of VSV. Then, the PSO-SVR model is validated by the subsequent flight data, and the verification results are compared with the traditional PSO-BP neural network model. Finally, the PSO-SVR model is applied to engine fault diagnosis. The results show that the regression prediction accuracy of the PSO-SVR model is better than that of the PSO-BP neural network model, and it can accurately reflect the VSV regulation rule. It can be used in the condition monitoring and fault dia-gnosis of engine, and can also provide reference for the design of VSV control system.