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SVR在航空发动机基线挖掘中的应用研究 被引量:11

The Application Research of Support Vector Regression in Aero-engine's Baseline Mining
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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。 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.
出处 《机械科学与技术》 CSCD 北大核心 2017年第1期152-160,共9页 Mechanical Science and Technology for Aerospace Engineering
基金 中央高校基本科研业务费专项资金项目(ZXH2012P007)资助
关键词 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 aero-engine's baseline SVR monitoring system of manufacturers QAR multi-parameter regression of SVR
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  • 1蒋亮,李书明,郝英,白杰,郝红勋.航空发动机气路故障诊断研究现状[J].中国民航大学学报,2005,23(z1):60-62. 被引量:6
  • 2陈大光,韩凤学,唐耿林.多状态气路分析法诊断发动机故障的分析[J].航空动力学报,1994,9(4):349-352. 被引量:34
  • 3钟诗胜,周志波,张永,康力平.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. 被引量:14
  • 4李长征,雷勇.航空发动机气路故障诊断[J].测控技术,2006,25(8):21-24. 被引量:3
  • 5Yaag Hui, Yan Qin. Artificial intelligence system for airplane engine trouble diagnosis [J]. JCAI, CHINA, 1994, 12(1) : 54-62.
  • 6Li Y G, Nilkitsaranont P. Gas turbine performance prognostic for condition-based maintenance[J]. Applied Energy,2009,86(10):2152-2161.
  • 7Naeem M, Singh R, Probert D. Implications of engine's deterioration upon an aero-engine HP turbine blade's thermal fatigue life[J]. International Journal of Fatigue, 2000,22(2) : 147-160.
  • 8Zedda M,Singh R. Gas turbine engine and sensor fault diagnosis using optimization techniques[J]. Journal of Propulsion and Power,2002,18(5):1019-1025.
  • 9Wang Haixia, Lee Jay, Ueda Takahiro, et al. Engine health assessment and prediction using the group method of data handling and the method of match matrix-Autoregressive moving average[J]. Proceedings of the ASME Turbo Expo, 2007,1: 697-702.
  • 10Jaw Link C. Recent advancements in aircraft Engine Health Management (EHM) technologies and recommendations for the next step[J]. Proceedings of the ASME Turbo Expo, 2005,1 : 683-695.

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