航空液压泵磨损状况预测

葛薇; 王少萍

航空液压泵磨损状况预测

葛薇,
王少萍

北京航空航天大学自动化科学与电气工程学院, 北京 100191

基金项目: 国家863计划资助项目(2009AA04Z412); 航空创新基金资助项目(08D51010); 111计划资助项目

详细信息

中图分类号: TP 271⁺.31
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出版历程
- 收稿日期: 2010-07-20
- 网络出版日期: 2011-11-30

Wear condition prediction of hydraulic pump

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 磨损是航空液压泵典型的渐进性故障之一,因磨损量难以测量,对磨损状况进行准确的预测比较困难.针对上述问题,提出了基于多尺度数据的支持向量机预测方法,该方法将支持向量机用于时间序列预测的基本理论和数据多尺度分解、相空间重构方法结合,能更有效地挖掘时间序列的内在联系及变化规律.采用回油流量作为反映航空液压泵磨损状况的敏感信号,将其分解为趋势项和随机项,采用多尺度支持向量机作等维信息一步预测和多步预测,利用网格方法对预测模型参数寻优.对比传统支持向量机算法分析其预测精度,结果表明:多尺度支持向量机模型预测精度更高,适于中长期预测.
- 航空液压泵 /
- 磨损预测 /
- 多尺度支持向量机 /
- 数据分解 /
- 相空间重构
Abstract: Wear is a typical progressive failure of aero hydraulic pump. It is difficult to measure wear loss. To solve precision wear condition prediction problem, multi-dimensional support vector machine (SVM) prediction method was proposed, based on theoretical basis of SVM applied to time series prediction, multi-dimensional data decomposition and phase space reconstruction. The inner relationship of time series can be mined and reflected more effectively by this method. Oil-return flow was chosen to reflect the wear condition of hydraulic pump and was decomposed into trend data and random data. Multi-dimensional SVM was applied to predict oil-return flow of the aero hydraulic pump one-step ahead and multi-step ahead with grid search optimization method. The results show that multi-dimensional SVM model has higher prediction precision and is very suitable for long-term forecasting compared with the predicted results of traditional SVM.
- aero hydraulic pump /
- wear prediction /
- multi-dimensional support vector machine /
- data decomposition /
- phase space reconstruction

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参考文献(1)

[1] 赵大庆.液压泵污染磨损与控制[M].北京:北京煤炭工业出版社,1993:1-50 Zhao Daqing.Pollution wear and control of pump [M].Beijing:China Coal Industry Publishing House,1993:1-50(in Chinese) [2] 张文超.机载液压泵源健康管理系统研究 .北京:北京航空航天大学自动化科学与电气工程学院,2008 Zhang Wenchao.Research on on-board PHM of hydraulic power system .Beijing: School of Automation Science and Electrical Engineering,Beijing University of Aeronautics and Astronautics,2008(in Chinese) [3] Wang Wenchuan,Chau Kwok-Wing,Cheng Chuntian,et al.A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series [J].Journal of Hydrology,2009,374(3-4):294-306 [4] Hippert H S,Pedreira C E,Souza R C.Neural networks for short-term load forecasting: a review and evaluation [J].Power Systems, 2001,16(1):44-55 [5] Cristianini N,Shawe-Taylor J.An introduction to support vector machines and other kernel-based learning method [M].Beijing:China Machine Press,2005:1-124 [6] Peng Xinjun,Wang Yifei.A normal least squares support vector machine(NLS-SVM) and its learning algorithm [J].Neurocomputing, 2009,72(16-18):3734-3741 [7] Li Lin,Ji Hongbing.Signal feature extraction based on an improved EMD method [J].Measurement, 2009,42(5):796-803 [8] Osowski S,Garanty K.Forecasting of the daily meteorological pollution using wavelets and support vector machine [J].Engineering Applications of Artificial Intelligence,2007,20(6):745-755 [9] 康卓,黄竞伟,李艳,等.复杂系统数据挖掘的多尺度混合算法[J].软件学报,2003,14(7):1229-1237 Kang Zhuo,Huang Jingwei,Li Yan,et al.A multi-scale mixed algorithm for data mining of complex system[J].Journal of Software,2003,14(7):1229-1237(in Chinese) [10] 杨叔子,吴雅,轩建平,等.时间序列分析的工程应用[M].2版.武汉:华中科技大学出版社,2007:1-20,175-222 Yang Shuzi,Wu Ya,Xuan Jianping,et al.Time series analysis in engineering application[M].2nd.Wuhan:Huazhong University of Science & Technology Press,2007:1-20,175-222(in Chinese) [11] Li Hongxing,Xu Li D.Feature space theory:a mathematical foundation for data mining [J].Knowledge-Based Systems,2001,14(5-6):253-257

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