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一种可用于航空发动机健康状态预测的动态集成极端学习机模型

钟诗胜 雷达

钟诗胜, 雷达. 一种可用于航空发动机健康状态预测的动态集成极端学习机模型[J]. 航空动力学报, 2014, 29(9): 2085-2090. doi: 10.13224/j.cnki.jasp.2014.09.010
引用本文: 钟诗胜, 雷达. 一种可用于航空发动机健康状态预测的动态集成极端学习机模型[J]. 航空动力学报, 2014, 29(9): 2085-2090. doi: 10.13224/j.cnki.jasp.2014.09.010
ZHONG Shi-sheng, LEI Da. A dynamic ensemble extreme learning machine model for aircraft engine health condition prediction[J]. Journal of Aerospace Power, 2014, 29(9): 2085-2090. doi: 10.13224/j.cnki.jasp.2014.09.010
Citation: ZHONG Shi-sheng, LEI Da. A dynamic ensemble extreme learning machine model for aircraft engine health condition prediction[J]. Journal of Aerospace Power, 2014, 29(9): 2085-2090. doi: 10.13224/j.cnki.jasp.2014.09.010

一种可用于航空发动机健康状态预测的动态集成极端学习机模型

doi: 10.13224/j.cnki.jasp.2014.09.010
基金项目: 

国家高技术研究发展计划(2012AA040911-1);国家自然科学基金(60939003)

详细信息
    作者简介:

    钟诗胜(1964-),男,江西龙南人,教授、博士生导师,博士,主要从事复杂装备健康管理研究.

  • 中图分类号: V263.6;TP183

A dynamic ensemble extreme learning machine model for aircraft engine health condition prediction

  • 摘要: 提出一种动态集成极端学习机模型用于航空发动机健康状态预测.采用AdaBoost.RT集成学习算法对极端学习机(ELM)进行集成,在训练时采用每个训练样本的近邻样本对ELM的局域性能进行评估;在预测时首先确定新样本在训练样本集中的近邻样本,然后根据ELM在近邻样本上的性能来赋予集成权值实现弱学习机的动态集成.以燃油流量为指标进行航空发动机健康状态预测,动态集成ELM模型短期预测结果的平均相对误差绝对值(MAPE)为3.688%,小于单一ELM模型的3.830%以及静态集成ELM模型的3.719%;长期预测结果中动态集成ELM模型的MAPE为3.075%,小于单一ELM模型的4.355%以及静态集成ELM模型的3.884%.因此动态集成ELM模型更适用于航空发动机健康状态预测.

     

  • [1] 丁刚,徐敏强,侯立国.基于过程神经网络的航空发动机排气温度预测[J].航空动力学报,2009,24(5):1035-1039. DING Gang,XU Minqiang,HOU Liguo.Prediction of aeroengine exhaust gas temperature using process neural network[J].Journal of Aerospace Power,2009,24(5):1035-1039.(in Chinese)
    [2] 金向阳,林琳,钟诗胜,等.航空发动机振动趋势预测的过程神经网络法[J].振动、测试与诊断,2011,31(3):331-334. JIN Xiangyang,LIN lin,ZHONG Shisheng,et al.Prediction of aeroengine vibration trend using process neural network[J].Journal of Vibration,Measurement and Diagnosis,2011,31(3):331-334.(in Chinese)
    [3] 陈果,杨虞微.航空发动机复杂磨损趋势的神经网络多变量预测模型[J].中国机械工程,2007(1):70-74. CHEN Guo,YANG Yuwei.Artificial neural network multi-variable forecasting model of aero-engine complex wear trend[J].China Mechanical Engineering,2007(1):70-74.(in Chinese)
    [4] Schapire R E.The strength of weak learnability[J].Machine Learning,1990,5(2):197-227.
    [5] Freund Y,Schapire R E.A desicion-theoretic generalization of on-line learning and an application to boosting[J].Computational Learning Theory,1995,904:23-37.
    [6] Breiman L.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
    [7] Solomatine D P,Shrestha D L.AdaBoost.RT:a boosting algorithm for regression problems[C]//Proceeding of 2004 IEEE International Joint Conference on Neural Networks.Budapest,Hungary:IEEE,2004:1163-1168.
    [8] Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70:489-501.
    [9] Huang G B,Wang D H,Lan Y.Extreme learning machines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122.
    [10] SHI Lichen,BAO Lianglu.EEG-based vigilance estimation using extreme learning machines[J].Neurocomputing,2013,102:135-143.
    [11] TIAN Huixin,MAO Zhizhong.An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace[J].IEEE Transactions on Automation Science and Engineering,2010,7(1):73-80.
    [12] Drucker H.Improving regressors using boosting techniques[C]//Proceeding of the 14th International Conference on Machine Learning.San Francisco:Morgan Kaufmann Publishers Inc,1997:107-115.
    [13] YANG Yihsuan,LIN Yuching,SU Yafan,et al.Music emotion classification:a regression approach[C]//Proceeding of 2007 IEEE International Conference on Multimedia and Expo.Beijing:IEEE,2007:208-211.
    [14] Niall R,Patterson D.A fusion of stacking with dynamic integration[C]//Proceeding of 20th International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann Publishers Inc,2007:2844-2849.
    [15] Puuronen S,Terziyan V,Tsymbal A.A dynamic integration algorithm for an ensemble of classifiers[C]//Proceeding of 11th International Symposium,Foundations of Intelligent Systems.Berlin Heidelberg:Springer,1999:592-600.
    [16] Alexey T,Puuronen S.Bagging and boosting with dynamic integration of classifiers[C]//Proceeding of 4th European Conference,Principles of Data Mining and Knowledge Discovery.Berlin Heidelberg:Springer,2000:116-125.
    [17] Niall R,Patterson D,Anand S,et al.Dynamic integration of regression models[C]//Proceeding of 5th International Multiple Classifier Systems Workshop.Berlin Heidelberg:Springer,2004:164-173.
    [18] ZHANG Chunxia,ZHANG Jiangshe.A local boosting algorithm for solving classification problems[J].Computational Statistics and Data Analysis,2008,52(4):1928-1941.
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出版历程
  • 收稿日期:  2013-06-01
  • 刊出日期:  2014-09-28

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