SVR在航空发动机基线挖掘中的应用研究

曹惠玲; 张卓; 曲春刚

doi:10.13433/j.cnki.1003-8728.2017.0122

SVR在航空发动机基线挖掘中的应用研究

doi: 10.13433/j.cnki.1003-8728.2017.0122

1.
中国民航大学航空工程学院, 天津 300300

基金项目:

中央高校基本科研业务费专项资金项目（ZXH2012P007）资助

详细信息

作者简介:
曹惠玲(1962-),教授,博士,研究方向为航空发动机性能分析与故障诊断,hlcao@cauc.edu.cn

计量
- 文章访问数: 249
- HTML全文浏览量: 31
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2015-03-08
- 刊出日期: 2017-01-16

The Application Research of Support Vector Regression in Aero-engine's Baseline Mining

1.
College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 针对航空发动机基线难以获取的问题，利用支持向量回归机（support vector regression，SVR）算法，采用厂家监控系统数据和飞机快速存储记录器（quick access recorder，QAR）数据两种方式对基线进行挖掘分析，提供了获取基线的多种途径和方法，取得了比较可靠的结果。支持向量回归机在处理非线性回归分析时具有快速、准确的优点，能够进行单参数及多参数的基线回归分析，通过计算结果比较分析，多参数基线回归与单参数基线回归、一元线性基线拟合相比具有偏差小、精度高的优势，能够有效提高发动机基线监控的准确性。
- 航空发动机基线 /
- 支持向量回归机 /
- 厂家监控系统 /
- 快速存储记录器 /
- 多参数基线回归
Abstract: As the aero-engine's baseline is difficult to get, Support Vector Regression (SVR) is used with data from monitoring systems of manufacturers and Quick Access Recorder (QAR) to do baseline mining analysis, providing a variety of ways to get baseline and the results turned out to have a good reliability. SVR is fast and precise in dealing with nonlinear regression analysis. Single parameter regression and multi-parameter regression can be processed by using SVR. The conclusion can be drawn from analysis of calculation results that multi-parameter regression of SVR has a lot of advantages such as small deviation and high accuracy compared with linear regression and single parameter regression of SVR. As a result, the accuracy of the aero-engine's monitoring can be improved effectively.
- aero-engine's baseline /
- SVR /
- monitoring system of manufacturers /
- QAR /
- multi-parameter regression of SVR

HTML全文

参考文献(16)

[1]	钟诗胜,崔智全,付旭云.Rolls-Royce发动机基线挖掘方法[J].计算机集成制造系统,2010,16(10):2265-2270 Zhong S S, Cui Z Q, Fu X Y. Baseline mining method of RR's engine[J]. Computer Integrated Manufacturing Systems, 2010,16(10):2265-2270(in Chinese)
[2]	钟诗胜,周志波,张永,等.基于三次回归分析的试车台基线库的建立[J].计算机集成制造系统,2005,11(2):270-274 Zhong S S, Zhou Z B, Zhang Y, et al. Construction of test cell baselines based on thrice regression analysis[J]. Computer Integrated Manufacturing Systems, 2005,11(2):270-274(in Chinese)
[3]	Luan S G, Zhong S S, Li Y. Hybrid recurrent process neural network for aero engine condition monitoring[J]. Neural Network World, 2008,18(2):133-145
[4]	北京飞机维修工程有限公司.发动机机群的科学管理[Z].北京:北京飞机维修工程有限公司,2000:121-130 AMECO, Beijing. Engine scientific management[Z]. Beijing:AMECO, Beijing, 2000:121-130(in Chinese)
[5]	林兆福,范作民.发动机基线方程的建立和应用[J].中国民航学院学报,1992,10(4):20-32 Lin Z F, Fan Z M. Development and application of engine baseline equations[J]. Journal of Civil Aviation University of China, 1992,10(4):20-32(in Chinese)
[6]	陕振勇.航空发动机气路参数偏差值模型研究与应用[D].哈尔滨:哈尔滨工业大学,2012 Shan Z Y. Research on aeroengine gas path parameter deviation model and its application[D]. Harbin:Harbin Institute of Technology, 2012(in Chinese)
[7]	钟诗胜,崔智全,王体春,等.基于偏差值的航空发动机参数标准化修正模型[J].航空动力学报,2012,27(11):2592-2598 Zhong S S, Cui Z Q, Wang T C, et al. Corrected standardization model of aero-engine parameter based on deviation[J]. Journal of Aerospace Power, 2012,27(11):2592-2598(in Chinese)
[8]	崔智全.民航发动机气路参数偏差值挖掘方法及其应用研究[D].哈尔滨:哈尔滨工业大学,2013 Cui Z Q. Civil aeroengine gas path parameter deviation mining method with application[D]. Harbin:Harbin Institute of Technology, 2013(in Chinese)
[9]	Vapnik V N.统计学习理论的本质[M].张学工,译.北京:清华大学出版社,2000 Vapnik V N. The essence of statistical learning theory[M]. Zhang X G, translate. Beijing:Tsinghua University Press, 2000(in Chinese)
[10]	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995,20(3):273-297
[11]	Suykens J A K, Van Gestel T, De Brabanter J, et al. Least squares support vector machines[M]. Singapore:World Scientific, 2002
[12]	邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004 Deng N Y, Tian Y J. New method in data mining[M]. Beijing:Science Press, 2004(in Chinese)
[13]	瞿红春,林兆福.民用航空燃气涡轮发动机原理[M].北京:兵器工业出版社,2006 Qu H C, Lin Z F. Principle of civil aviation gas turbine engine[M]. Beijing:The Publishing House of Ordnance Industry, 2006(in Chinese)
[14]	彭云飞.航空发动机状态参数处理技术及其应用研究[D].哈尔滨:哈尔滨工业大学,2009 Peng Y F. Research on processing technique of aeroengine condition parameters and its application[D]. Harbin:Harbin Institute of Technology, 2009(in Chinese)
[15]	孔成安,李文华,尹湛.利用QAR数据实施飞机性能监控[J].中国民用航空,2008,(10):54,56 Kong C A, Li W H, Yin Z. Monitoring aircraft performance with QAR data[J]. China Civil Aviation, 2008,(10):54,56(in Chinese)
[16]	刘志荣,朱睿,梁忠生,等.发动机健康基线及评估准则研究[J].厦门大学学报(自然科学版),2010,49(4):520-525 Liu Z R, Zhu R, Liang Z S, et al. Research of engine health baselines and evaluation criterion[J]. Journal of Xiamen University (Natural Science), 2010,49(4):520-525(in Chinese)