Volume 32 Issue 12
Dec.  2017
Turn off MathJax
Article Contents
Article Navigation >  Journal of Aerospace Power  > 2017  >  32(12): 3022-3030
Forecasting of aeroengine performance trend based on fuzzy information granulation and optimized SVM[J]. Journal of Aerospace Power, 2017, 32(12): 3022-3030. doi: 10.13224/j.cnki.jasp.2017.12.027
Citation: Forecasting of aeroengine performance trend based on fuzzy information granulation and optimized SVM[J]. Journal of Aerospace Power, 2017, 32(12): 3022-3030. doi: 10.13224/j.cnki.jasp.2017.12.027
shu

Forecasting of aeroengine performance trend based on fuzzy information granulation and optimized SVM

doi: 10.13224/j.cnki.jasp.2017.12.027
  • Received Date: 2016-05-17
  • Publish Date: 2017-12-28
    Abstract
  • A method to predict the change trend and space of aeroengine parameters with fuzzy information granulation (FIG) and optimized support vector machine (SVM) was put forward. FIG was adopted to granulate the parameters. Genetic algorithm (GA) was applied into adaptive selection of the best penalty parameter and kernel function parameter with Kfold cross validation (KCV) error minimum as the optimization goal. The SVM model was trained for nonlinear prediction of fuzzy particles. The verification results of some airlines monitoring performance parameters data of an aeroengine showed that the algorithm proposed can effectively realize the change trend and spatial prediction of aeroengine performance parameters. In addition, the influence of window size on prediction accuracy and the effect of multistep prediction were studied on the basis of instance. As a result, it was concluded that the best window size was three data and the forecasting error within three steps was less than 10%.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    尉询楷,杨立,刘芳,等.航空发动机预测和健康管理［M］.北京:国防工业出版社,2014.
    [2]
    崔文斌,叶志锋,彭利方.基于信息融合遗传算法的航空发动机气路故障诊断［J］.航空动力学报,2015,30(5):1275-1280.CUI Wenbin,YE Zhifeng,PENG Lifang.Aeroengine gas path fault diagnosis based on genetic algorithm of information fusion［J］.Journal of Aerospace Power,2015,30(5):1275-1280.(in Chinese)
    [3]
    吴娅辉,李新良,洪宝林,等.基于SVM和广义粗糙度特征的航空发动机振动故障诊断方法［J］.航空动力学报,2011,26(11):2445-2449.WU Yahui,LI Xinliang,HONG Baolin,et al.Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature［J］.Journal of Aerospace Power,2011,26(11):2445-2449.(in Chinese)
    [4]
    李艳军,左洪福,吴振峰.基于磨粒分析方法的发动机磨损故障智能诊断技术［J］.南京航空航天大学学报,2001,33(3):221-226.LI Yanjun,ZUO Hongfu,WU Zhenfneg.Intelligent diagnostics for engine wear failure based on debris analysis［J］.Journal of Nanjing University of Aeronautics and Astronautics,2001,33(3):221-226.(in Chinese)
    [5]
    范庚,马登武.基于组合优化相关向量机的航空发动机性能参数概率预测方法［J］.航空学报,2013,34(9):2110-2121.FAN Geng,MA Dengwu.Probabilistic prediction method for aeroengine performance parameters based on combined optimum relevance vector machine［J］.Acta Aeronautica et Astronautica Sinica,2013,34(9):2110-2121.(in Chinese)
    [6]
    李晓白,崔秀伶,郎荣玲.航空发动机性能参数预测方法［J］.北京航空航天大学学报,2008,34(3):253-256.LI Xiaobai,CUI Xiuling,LANG Rongling.Forecasting method or aeroengine performance parameters［J］.Journal of Beijing University of Aeronautics and Astronautics,2008,34(3):253-256.(in Chinese)
    [7]
    GAN M L,YANG Z.Timeseries modeling and fault forecast study spectral analysis of lubricating oil［J］.Transactions of Nanjing University of Aeronautics and Astronautics,2001,18(1):86-90.
    [8]
    陈果.用结构自适应神经网络预测航空发动机性能趋势［J］.航空学报,2007,28(3):535-539.CHEN Guo.Forecasting engine performance trend by using structure selfadaptive neural network［J］.Acta Aeronautica et Astronautica Sinica,2007,28(3):535-539.(in Chinese)
    [9]
    丁刚,付旭云,钟诗胜.基于过程神经网络的航空发动机性能参数预测［J］.计算机集成制造系统,2011,17(1):198-207.DING Gang,FU Xuyun,ZHONG Shisheng.Aeroengine performance parameters prediction based on process neural network［J］.Computer Integrated Manufacturing Systems,2011,17(1):198-207.(in Chinese)
    [10]
    杨虞微,陈果.基于结构自适应径向基神经网络的油样光谱数据建模［J］.仪器仪表学报,2006,27(1):98-101.YANG Yuwei,CHEN Guo.Modeling of spectrometric oil analysis data based on structure selfadaptive RBF neural network［J］.Chinese Journal of Scientific Instrument,2006,27(1):98-101.(in Chinese)
    [11]
    郭阳明,姜红梅,翟正军.基于灰色理论的自适应多参数预测模型［J］.航空学报,2009,30(5):925-931.GUO Yangming,JIANG Hongmei,ZHAI Zhengjun.Adaptive multiparameter prediction model based on grey theory［J］.Acta Aeronautica et Astronautica Sinica,2009,30(5):925-931.(in Chinese)
    [12]
    崔立志.灰色预测技术及其应用研究［D］.南京:南京航空航天大学,2010.CUI Lizhi.Grey forecast technology and its application research［D］.Nanjing:Nanjing University of Aeronautics and Astronautics,2010.(in Chinese)
    [13]
    曾波.灰色预测建模技术研究［D］.南京:南京航空航天大学,2011.ZENG Bo.Research on modeling technologies of grey prediction［D］.Nanjing:Nanjing University of Aeronautics and Astronautics,2011.(in Chinese)
    [14]
    尉询楷,李应红,王硕,等.基于支持向量机的航空发动机滑油监控分析［J］.航空动力学报,2004,19(3):392-397.WEI Xunkai,LI Yinghong,WANG Shuo,et al.Aeroengine lubrication monitoring analysis via support vector machines［J］.Journal of Aerospace Power,2004,19(3):392-397.(in Chinese)
    [15]
    张弦,王宏力.嵌入维数自适应最小二乘支持向量机状态时间序列预测方法［J］.航空学报,2010,31(12):2309-2314.ZHANG Xian,WANG Hongli.Condition time series prediction using least squares support vector machine with adaptive embedding dimension［J］.Acta et Aeronauticaet Astronautica Sinica,2010,31(12):2309-2314.(in Chinese)
    [16]
    李本威,张赟,孙涛.基于免疫粒子群算法的滑油屑末支持向量机预测模型设计［J］.航空动力学报,2009,24(7):1639-1643.LI Benwei,ZHANG Yun,SUN Tao.Design of forecasting model for aero engine lubrication debris support vector machines based on immuneparticle swarm optimization algorithm［J］.Journal of Aerospace Power,2009,24(7):1639-1643.(in Chinese)
    [17]
    王小川,史峰,郁磊,等.MTALAB神经网络 30个案例分析［M］.北京:北京航空航天大学出版社,2010.
    [18]
    BARGIELA A,PEDRYCZ W.Granular computing:an introduction［M］.Dordrecht:Kluwer Academic Publishers,2003.
    [19]
    CORTES C V.Support vector networks［J］.Machine Learning,1995,20(3):273-293.
    [20]
    VAPNIK V N.The nature of statistical learning theory［M］.Berlin:SpringerVerlag,1995.
    [21]
    吴玫,陆金桂.遗传算法的研究进展综述［J］.机床与液压,2008,36(3):176-179.WU Mei,LU Jingui.Summary of research progress of the genetic algorithms［J］.Machinetool and Hydraulics,2008,36(3):176-179.(in Chinese)
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF(914.0KB)
    XML

    Article Metrics

    Article views (614) PDF downloads(445) Cited by()
    Proportional views
    Related

    Copyright © 《Journal of Aerospace Power》 All Rights Reserved.

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return