摘要
针对脉冲噪声和随机噪声对经验模式分解所产生的模式混叠和端点效应问题,提出了一种中值滤波-奇异值分解(Median Filter-Singular Value Decomposition,简称MF-SVD)联合降噪的优化经验模式分解方法.利用中值滤波对脉冲噪声的去除能力和SVD对随机噪声的抑制能力,同时为了解决SVD降噪中降噪阶次难以确定的问题,提出了能量差分谱的概念改善了SVD降噪能力.联合中值滤波和改进后的SVD降噪方法去除脉冲噪声和随机噪声干扰,有效改善经验模式分解质量.将该方法应用到航空发动机振动试飞数据分析中,很好地获取了表征自由涡轮转子和燃气发生器转子振动特征的数据分量,有效抑制了模式混叠和端点效应,验证了优化经验模式分解方法应用的有效性.
For the mode mixing and end effect of empirical mode decomposition （EMD）due to the dis-turbance of impulse noise and random noise,a new method of optimized EMD is proposed based on me-dian filter-singular value decomposition （MF-SVD）de-noising.The impulse noise could be removed by the median filter and the random noise could be reduced by SVD.In order to determine the de-noising order of SVD,the energy difference spectrum was introduced to improve the de-noising capability of SVD.The median filter and improved SVD de-noising method were combined to reduce the impulse noise and random noise,and the decomposition quality of EMD was improved effectively.Then this method was applied to analyze vibration test data of aero-engine,data components representing free power turbine rotor and gas generator rotor vibration characteristics were well obtain and the mode mix-ing and end effect were restrained effectively in this paper.Therefore,optimized EMD is proved to be ef-fective.
出处
《测试技术学报》
2015年第3期271-276,共6页
Journal of Test and Measurement Technology
