%0 Journal Article
%T 应用支持向量回归机探索发动机VSV调节规律<br>Exploration of engine VSV regulation law using support vector regression
%A 曹惠玲
%A 阚玉祥
%A 薛鹏
%J 北京航空航天大学学报
%D 2018
%R 10.13700/j.bh.1001-5965.2017.0523
%X 摘要 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。<br>Abstract：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.
%K 发动机可调静子叶片(VSV)
%K 调节规律
%K 支持向量回归机(SVR)
%K 粒子群优化(PSO)算法
%K 快速存取记录装置(QAR)数据
%K 故障诊断&lt
%K br&gt
%K engine variable stator vane (VSV)
%K regulation law
%K support vector regression (SVR)
%K particle swarm optimization (PSO) algorithm
%K quick access recorder(QAR) data
%K fault diagnosis
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14522.shtml