应用支持向量回归机探索发动机VSV调节规律

曹惠玲; 阚玉祥; 薛鹏

doi:10.13700/j.bh.1001-5965.2017.0523

应用支持向量回归机探索发动机VSV调节规律

doi: 10.13700/j.bh.1001-5965.2017.0523

1.
中国民航大学航空工程学院, 天津 300300
2.
中国民航大学工程技术训练中心, 天津 300300

基金项目:

中央高校基本科研业务费专项资金 3122014D010

详细信息

作者简介:
曹惠玲   女，博士，教授，硕士生导师。主要研究方向：航空发动机性能分析与故障诊断

阚玉祥   男, 硕士研究生。主要研究方向：航空发动机故障诊断

薛鹏   男, 硕士, 讲师。主要研究方向：航空发动机故障诊断

通讯作者:
曹惠玲.E-mail:hlcao@cauc.edu.cn

中图分类号: V263.6;TP206
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出版历程
- 收稿日期: 2017-08-11
- 录用日期: 2017-12-04
- 网络出版日期: 2018-07-20

Exploration of engine VSV regulation law using support vector regression

1.
College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
Engineering Technology Training Center, Civil Aviation University of China, Tianjin 300300, China

Funds:

the Fundamental Research Funds for the Central Universities 3122014D010

More Information

Corresponding author: CAO Huiling.E-mail:hlcao@cauc.edu.cn

摘要

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。
- 发动机可调静子叶片(VSV) /
- 调节规律 /
- 支持向量回归机(SVR) /
- 粒子群优化(PSO)算法 /
- 快速存取记录装置(QAR)数据 /
- 故障诊断
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.
- engine variable stator vane (VSV) /
- regulation law /
- support vector regression (SVR) /
- particle swarm optimization (PSO) algorithm /
- quick access recorder(QAR) data /
- fault diagnosis

HTML全文

图 1 PSO算法流程图

Figure 1. Flowchart of PSO algorithm

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图 2 SVR模型训练相关系数和测试相关系数随惩罚参数的变化(σ=5)

Figure 2. Variation of SVR model training correlation coefficient and test correlation coefficient with penalty parameter(σ=5)

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图 3 SVR模型训练相关系数和测试相关系数随核参数的变化(C=300)

Figure 3. Variation of SVR model training correlation coefficient and test correlation coefficient with kernel function parameter(C=300)

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图 4 PSO算法进化曲线

Figure 4. Evolution curves of PSO algorithm

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图 5 PSO-SVR模型训练的相对误差率

Figure 5. Relative error rate of PSO-SVR model training

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图 6 2个航班的监控结果

Figure 6. Monitoring results for two flights

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表 1 各参数与SVA的相关系数

Table 1. Correlation coefficient of each parameter with SVA

参数	相关系数
N1 CMD	0.986
N1	0.986
Tt₃	0.973
N2	0.939
Ma	0.901
Tt₂₅	0.895
ALTC	0.871
ALT	0.871
TRA	0.820
BP	0.778
WF	0.702
TAT	-0.750
Pt₅	-0.767
Tt₂	-0.809
SAT	-0.851
Pt₂	-0.864

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表 2 4个航班的预测结果

Table 2. Prediction results of four flights

验证航班	PSO-SVR模型		PSO-BP神经网络模型
验证航班	MSE	R²/%	MSE	R²/%
a	8.20×10^-4	99.57	1.05	97.60
b	6.08×10^-4	99.76	0.65	99.03
c	1.00×10^-3	99.67	1.34	98.69
d	7.80×10^-3	99.69	0.97	99.08

下载: 导出CSV

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