期刊文献+

基于CEMD的燃油消耗率提取方法 被引量:2

Research of Fuel Consumption Extraction Using Complex Empirical Mode Decomposition
下载PDF
导出
摘要 针对飞参系统记录的剩余燃油信号量化噪声较大且呈非线性、非平稳性的特点以及经验模态分解(empirical mode decomposition,简称EMD)中存在的模态混叠给燃油消耗率提取带来的问题,提出了基于复数据经验模态分解(complex empirical mode decomposition,简称CEMD)的燃油消耗率提取方法。首先,提取记录信号中的关键信息,并利用非线性支持向量回归构造与真实信号形态上接近的模拟信号;然后,在CEMD中利用模拟信号来指导记录信号同步分解以减小模态混叠;最后,从分解结果中估算真实的剩余燃油信息并对其求一阶导数得到燃油消耗率。仿真结果表明,该方法相对于其他方法具有明显的性能优势,可以提取出精确的燃油消耗率参数。 A method of fuel consumption extraction based on complex empirical mode decomposition is proposed to solve the fuel consumption extraction problems resulting from high quantization noise,and nonlinear and non-stationary characteristics of residual fuel volume recorded by a flight data recorder,as well as the mode mixing in empirical mode decomposition.First,a simulated signal with a similar morphology to the real signal is constructed with nonlinear support vector regression using the recorded signal's key message.Second,the mode mixing can be reduced by using a simulant signal to synchronously guide the decomposition of the recorded signal in complex empirical mode decomposition.Finally,the fuel consumption is equivalent to the first-order derivative of real residual fuel volume estimated from the decomposition results.Simulation results show that this method has more advantages compared with other methods,and is adequate for extracting precious fuel consumption.
出处 《振动.测试与诊断》 EI CSCD 北大核心 2015年第5期902-907,992,共6页 Journal of Vibration,Measurement & Diagnosis
基金 国家自然科学基金资助项目(61372027)
关键词 经验模态分解 模态混叠 复数据经验模态分解 非线性支持向量回归 燃油消耗率 empirical mode decomposition(EMD) mode mixing complex empirical mode decomposition(CEMD) nonlinear support vector regression fuel consumption
  • 相关文献

参考文献16

  • 1Senzig D A, Fleming G G, Iovinelli R J. Modeling of terminal-area airplane fuel eonsumption[J]. Journal of Aircraft, 2009, 46(4): 1089-1093.
  • 2Oaks R D, Paglione M. Prototype implementation and concept validation of a 4-D trajectory fuel burn model application [J]. Journal of American Institute of Aeronautics and Astronautics, 2010, 8164: 2-5.
  • 3Muhammad N. Implications of high-pressure turbine's erosion for a military turbofan's fuel eonsumption[J]. Journal of Aerospace Engineering, 2012, 25 ( 1 ): 108-116.
  • 4冯广斌,吴震宇,袁惠群.基于混沌理论与SVM的内燃机振动信号趋势预测[J].振动.测试与诊断,2011,31(1):64-69. 被引量:13
  • 5Huang N E, Norden E, Long S R,et al. The empirical mode decomposition method and the hilbert spectrum for non-stationary time series analysis ~- J ~. Proceedings of the Royal Society of London, 1998, 454(1971): 903 -995.
  • 6胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,31(4):429-434. 被引量:158
  • 7Wu Zhaohua, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1 (1) : 1-41.
  • 8陈隽,李想.运用总体经验模态分解的疲劳信号降噪方法[J].振动.测试与诊断,2011,31(1):15-19. 被引量:28
  • 9陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动.测试与诊断,2012,32(4):542-546. 被引量:110
  • 10Tanaka T, Mandic D P. Complex empirical mode de- composition[J]. Signal Processing Letters, 2006, 14 (2) : 101-104.

二级参考文献51

共引文献290

同被引文献11

引证文献2

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部