Volume 27 Issue 12
Dec.  2012
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WANG Hua-wei, WU Hai-qiao. Residual useful life prediction for aircraft engine based on information fusion[J]. Journal of Aerospace Power, 2012, 27(12): 2749-2755.
Citation: WANG Hua-wei, WU Hai-qiao. Residual useful life prediction for aircraft engine based on information fusion[J]. Journal of Aerospace Power, 2012, 27(12): 2749-2755.

Residual useful life prediction for aircraft engine based on information fusion

  • Received Date: 2011-11-24
  • Publish Date: 2012-12-28
  • The monitoring information has been utilized and fused for performance degradation evaluation.In consideration of random and error information, Bayesian linear model has been used for performance degradation evaluation of aircraft engine.The result of the performance degradation evaluation has been seen as input variable, and the reliability evaluation model has been built based on gamma random process which was used for forecasting residual life under predetermined reliability index.For example, the effect of different monitoring parameters on residual useful life prediction was analyzed.The method can integrate performance monitoring with reliability analysis into one framework, which utilizes monitoring information adequately. As a result of above, the goal of an accurate prediction of residual useful life has been achieved, which accords with the demand of maintenance risk control for aircraft engines.

     

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  • [1]
    Cobel J B.Merging data sources to predict remaining useful life:an automated method to identify prognostic parameters[D].Knoxville:The University of Tennessee,2010.
    [2]
    Liao H,Zhao W,Guo H.Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model[C]//Proceedings of the Reliability and Maintainability Symposium(RAMS).California:IEEE Conference Publications,2006:127-132.
    [3]
    Vlokp J,Wnek M,Zygmunt M.Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions[J].Mechanical System and Signal Processing,2004,18(4):833-847.
    [4]
    Wang W.A model to predict the residual life of rolling element bearings given monitored condition information to date[J].IMA Journal of Management Mathematics,2002,13(1):3-16.
    [5]
    王英,王文彬,方淑芬,等.状态维修两阶段预知模型研究[J].哈尔滨工业大学学报,2007,28(11):1278-1281.WANG Ying,WANG Wenbin,FANG Shufen,et al.A two-stage prediction model research on condition-based maintenance[J].Journal of Harbin Engineering University,2007,28(11):1278-1281.(in Chinese)
    [6]
    Gebraeel N Z,Lawly M A,Li R,et al.Residual-life distributions from component degradation signals:a Bayesian approach[J].Institute of Industrial Engineers Trans.,2005,37(6):543-557.
    [7]
    Baruah P,Chinnam R B.HMMs for diagnostics and prognostics in machining process[J].International Journal of Production Research,2003,43(6):1275-1293.
    [8]
    Camci F.Process monitoring,diagnosis and prognosis using support vector machines and hidden Markov models[D].Detorit:Graduate School of Wanye State University,2005.
    [9]
    Li Y G,Nilkitsaranont P.Gas turbine performance prognostic for condition-based maintenance[J].Applied Energy,2009,86(10):2152-2161.
    [10]
    Yam R C M,Tse P W,Li L,Tu P.Intelligent predictive decision support system for condition-based maintenance[J].Int. J Adv. Manuf. Technology,2001,17(5):383-391.
    [11]
    Byington C S,Watson M,Edwards D.Data-driven network methodology to remaining life predictions for aircraft actuator components[C]//Proceedings of the IEEE Aerospace Conf.MT(USA):IEEE Conference Publications,2004:3581-3589.
    [12]
    Gebraeel N Z,Lawley M A.A neural network degradation model for computing and updating residual life distributions[J].IEEE Transactions on Automation Science and Engineering,2008,5(1):387-401.
    [13]
    Przytula K W,Choi A.Reasoning framework for diagnosis and prognosis[C]//Proceedings of 2007 IEEE Aerospace conference.Big Sky,MT(USA):IEEE Conference Publications,2007:3-10.
    [14]
    Dong M.Yang Z B.Dyanmic Bayesian network based prognosis in machining process[J].Journal of Shanghai Jiaotong University,2008,13(3):318-322.
    [15]
    Goldstein M,Wooff D.Bayes linear statistics,theory & methods[M].USA:Wiley Publiser,2007.
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