基于边界值的多元混沌发动机性能预测算法

史永胜; 彭朋; 宋云雪

基于边界值的多元混沌发动机性能预测算法

1.
中国民航大学航空工程学院,天津 300300
2.
深圳航空有限责任公司维修工程部,广东深圳 518128

基金项目: 国家自然科学基金委员会与中国民用航空总局联合资助(60879017,61179048); 天津市支撑计划项目(重点)(11ZCKFGX04100); 航空科学基金(20110267001)

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出版历程
- 收稿日期: 2011-01-25
- 修回日期: 2011-06-29
- 刊出日期: 2012-01-28

Multivariable chaotic arithmetic for aero-engine performance forecasting based on boundary values

1.
College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
Maintenance and Engineering Department, Shenzhen Airlines, Shenzhen Guangdong 518128, China

摘要

摘要: 首先提取航空发动机排气温度数据序列的边界值,并且证明了边界值序列具有混沌特征.其次提出了一种基于多元相空间重构的发动机状态混沌预测算法,实现对排气温度的预测.通过检验排气温度预测值是否超过所规定的红线,从而进行发动机的健康状态排查.作为验证实例,使用一组某机型发动机实际飞行数据对该预测算法进行了验证,结果表明:该组合算法降低了预测模型的时间复杂度,并大大提高了疑似异常点的预测精度.该方法可以为这种机型发动机故障预测提供决策依据.
- 航空发动机 /
- 边界提取 /
- 排气温度 /
- 多变量相空间重构 /
- 预测
Abstract: Firstly,the boundary values of exhaust gas temperature(t_eg) was extracted from the original exhaust gas temperature data series.Further analysis on the boundary values data series indicated that there existed a chaos feature in it.Then,chaotic forecasting arithmetic based on multivariable phase space reconstruction was established to forecast the data series.Finally,by comparing the series with the red line,the condition of aero-engine was defined.The proposed arithmetic was verified through some type of aircraft aero-engine t_eg data series obtained from actual flight.The result shows that the proposed arithmetic can reduce the time complexity and has a better forecast accuracy in the prediction of outliers.It can be used as a supporting model in the decision making of this aero-engine fault forecast.
- aero-engine /
- boundary values extraction /
- exhaust gas temperature /
- multivariable phase space reconstruction /
- forecasting

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参考文献(13)

[1]	Banks J,Reichard K,Crow E,et al.How engineers can conduct cost-benefit analysis for PHM systems[J].IEEE Aerospace and Electronic Systems Magazine,2009,24(3):22-30.
[2]	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 for aeroengine performance parameters[J].Journal of Beijing University of Aeronautics and Astronautics,2008,34(3):253-256.(in Chinese)
[3]	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):535-935.(in Chinese)
[4]	宋云雪,张科星,史永胜.基于多元线性回归的发动机性能参数预测研究[J].航空动力学报,2009,24(2):427-431. SONG Yunxue,ZHANG Kexing,SHI Yongsheng.Research on aeroengine performance parameters forecast based on multiple linear regression forecasting method[J].Journal of Aerospace Power,2009,24(2):427-431.(in Chinese)
[5]	刘林刚,李学仁,陈永刚.基于支持向量机的航空发动机振动预测模型研究[J].微计算机信息,2008,24(6):289-291. LIU Lingang,LI Xueren,CHEN Yonggang.Aero-engine vibration forecasting based on support vector machine [J] Microcomputer Information,2008,24(2):289-291.(in Chinese)
[6]	宋云雪,彭朋,史永胜.民用发动机状态混沌预测算法[J].航空动力学报,2011,26(3):675-679. SONG Yunxue,PENG Peng,SHI Yongsheng.Chaos arithmetic for civil aeroengine forecasting[J].Journal of Aerospace Power,2011,26(3):675-679.(in Chinese)
[7]	黎敏,徐金梧,阳建宏.基于多变量相重构的混沌时间序列预测[J].北京科技大学学报,2008,30(2):208-211. LI Min,XU Jinwu ,YANG Jianhong.Prediction for chaotic time series based on phase reconstruction of multivariate time series[J].Journal of University of Science and Technology Beijing,2008,30(2):208-211.(in Chinese)
[8]	Basharat A,Shah M.Time series prediction by chaotic modeling of nonlinear dynamical systems //Proceedings of the IEEE International Conference on Computer Vision.Kyoto,Japan:Institute of Electrical and Electronics Engineers Inc.,2009:1941-1948.
[9]	Rontani D,Locquet A,Sciamanna M.Time-delay identification in a chaotic semiconductor laser with optical feedback:a dynamical point of view[J].IEEE Journal of Quantum Electronics,2009,45(7):879-891.
[10]	Mirmomeni M,Lucas C.Model based method for determining the minimum embedding dimension from solar activity chaotic time series[J].International Journal of Engineering,Transactions A:Basics,2008,21(1):31-44.
[11]	Woolley J W,Agarwal P K,Baker J.Modeling and prediction of chaotic systems with artificial neural networks[J].International Journal for Numerical Methods in Fluids,2010,63(8):989-1004.
[12]	Kang S M,Guo Y N,Cheng W B.Application of improved adding-weight one-rank local-region method in electric power system short-term load forecasting //1st International Conference on Sustainable Power Generation and Supply.Nanjing,China:IEEE Computer Society,2007:1-4.
[13]	Wei X K,Li Y H,Zhang P.Analysis and application of support vector forecasting model based on chaos theory //Proceedings of Fifth World Congress on Intellingent Control and Automation.Hangzhou,China:Institute of Electrical and Electronics Engineers Inc.,2004:1847-1852.