Volume 29 Issue 12
Dec.  2014
Turn off MathJax
Article Contents
Article Navigation >  Journal of Aerospace Power  > 2014  >  29(12): 2930-2935
HOU Kuan-xin, DING Fa-jun, ZHANG Dao-xin, HUANG Xuan-hong, WEI Wu-guo. Sensor fault diagnosis of aero-engine based on DE-RLSSVM algorithm[J]. Journal of Aerospace Power, 2014, 29(12): 2930-2935. doi: 10.13224/j.cnki.jasp.2014.12.020
Citation: HOU Kuan-xin, DING Fa-jun, ZHANG Dao-xin, HUANG Xuan-hong, WEI Wu-guo. Sensor fault diagnosis of aero-engine based on DE-RLSSVM algorithm[J]. Journal of Aerospace Power, 2014, 29(12): 2930-2935. doi: 10.13224/j.cnki.jasp.2014.12.020
shu

Sensor fault diagnosis of aero-engine based on DE-RLSSVM algorithm

doi: 10.13224/j.cnki.jasp.2014.12.020

  • 1. Aviation Engineering College, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China;

  • 2. Aircraft Repair and Overhaul Plant, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China;

  • 3. Guanghan Sub-College, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China

  • Received Date: 2013-08-14
  • Publish Date: 2014-12-28
    Abstract
  • In order to improve the generalization capability and sparsity of reduced least square support vector machine (RLSSVM), DE-RLSSVM was presented, whereby differential evolution (DE) algorithm was used to select the support vector of RLSSVM. Experiments on benchmark regression data sets show that the presented algorithm has good generalization and sparsity capabilities. Then, the presented algorithm was applied to sensor fault diagnosis of aero-engine. DE-RLSSVM algorithm was used to detected sensors faults, then to locate and isolate the faults. The simulations show that the sensor fault diagnosis system using the presented algorithm can detect and isolate hardware faults of sensor of aero-engine.

     

    • FullText(HTML)
  • loading
    • References(18)
  • [1]
    Garg S.Controls and health management technologies for intelligent aerospace propulsion systems[R].AIAA-2004-0949,2004.
    [2]
    Link C.Recent advancements in aircraft engine health management (EHM) technologies and recommendations for next step[R].ASME Paper GT2005-68625,2005.
    [3]
    Delaat J C,Merrill W C.Advanced detection,isolation,and accommodation of sensor failures in turbofan engines[R].NASA Technical Paper-2925,1990.
    [4]
    Garg S,Schadow K,Horn W,et al.Sensor and actuator needs for more intelligent gas turbine engines[R].NASA/TM-2010-216746,2010.
    [5]
    XU Qihua,SHI Jun.Fault diagnosis for aero-engine applying a new multi-class support vector algorithm[J].Chinese Journal of Aeronautics,2006,19(1):175-182.
    [6]
    Alag G,Gilyard G.A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine[R].AIAA 90-1920,1990.
    [7]
    Kobayashi T,Simon D L.Application of a bank of Kalman filters for aircraft engine fault diagnositcs[R].NASA/TM-2003-212526,2003.
    [8]
    Mattern D L,Jaw L C,Guo T H,et al.Using neural networks for sensor validation[R].AIAA 98-3547,1998.
    [9]
    鲁峰,黄金泉.航空发动机部件性能参数融合预测[J].航空学报,2009,30(10):1795-1800. LU Feng,HUANG Jinquan.Engine component performance prognostics based on decision fusion[J].Acta Aeronautica et Astronautica Sinica,2009,30(10):1795-1800.(in Chinese)
    [10]
    Pecht M G.Prognostics and health management of electronics[M].Hoboken,US:John Wiley and Sons,2008.
    [11]
    Tobon-Mejia D A,Medjiaher K,Zerhouni N,et al.A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models[J].IEEE Transactions on Reliability,2012,61(2):491-503.
    [12]
    李晗,萧德云.基于数据驱动的故障诊断方法综述[J].控制与决策,2011,26(1):1-9. LI Han,XIAO Deyun.Survey on data driven fault diagnosis methods[J].Control and Decision,2011,26(1):1-9.(in Chinese)
    [13]
    Zhao J,Chen B,Shen J.A hybrid ANN-ES system for dynamic fault diagnosis of hydrocracking process[J].Computers and Chemical Engineering,1997,21(20):929-933.
    [14]
    Vapnik V N.The nature of statistical theory[M].New York:Springer-Verlag,1995.
    [15]
    Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
    [16]
    李业波,李秋红,黄向华,等.航空发动机传感器故障与部件故障诊断技术[J].北京航空航天大学学报,2013,39(9):1174-1180. LI Yebo,LI Qiuhong,HUANG Xianghua,et al.Fault diagnosis for sensors and components of aero-engine[J].Journal of Beijing University of Aeronautics and Astronautics,2013 39(9):1174-1180.(in Chinese)
    [17]
    Lee J,Lee D.An improved cluster labeling method for support vector clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):461-464.
    [18]
    Storn R,Price K.Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
    • 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(892.0KB)
    XML

    Article Metrics

    Article views (2113) PDF downloads(981) 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