集成经验模态分解中加入白噪声的自适应准则

英文篇名：Adaptive Guideline of Ensemble Empirical Mode Decomposition with Gauss White Noise
中文刊名：振动.测试与诊断
英文刊名：Journal of Vibration ; Measurement & Diagnosis
作者：蔡艳平 ; 李艾华 ; 徐斌 ; 许平 ; 何艳萍
英文作者：Cai Yanping1 ; Li Aihua1 ; Xu Bin1 ; Xu Ping1 ; 2 ; He Yanping3(1Department No.5 ; the Second Artillery Engineering College Xi′an ; 710025 ; China)(2State Key Laboratory for Manufacturing Systems Engineering ; Xi′an Jiaotong University Xi′an ; 710049 ; China)(3Department No.6 ; the Second Artillery Engineering College Xi′an ; 710025 ; China)
中文关键词：旋转机械 ; 故障诊断 ; 集成经验模态分解 ; 模态混叠
英文关键词：rotating machinery fault diagnosis empirical mode decomposition(AEEMD) mode mixing
出版日期：2011-12-15
机构：第二炮兵工程学院五系;西安交通大学机械制造系统工程国家重点实验室;第二炮兵工程学院六系;
年：2011
期：06
出版单位：振动.测试与诊断

摘要

现有集成经验模态分解(ensemble empirical mode decomposition,简称EEMD)算法中加入白噪声的大小与集成的次数都需要人为按照经验设定,缺乏可靠性。针对此问题,提出了自适应集成经验模态分解(adaptive ensemble empirical mode decomposition,简称AEEMD)算法,并给出了一种在EEMD方法中有效加入白噪声的可依据准则。首先,计算出输入信号的幅值标准差;然后,采用高通滤波方法对输入信号进行分解,通过计算高通滤波分解后的高频分量幅值标准差和输入信号幅值标准差来确定加入白噪声的幅值标准差,在此基础之上,EEMD集成次数根据期望的信号分解相对误差和加入白噪声的幅值标准差惟一确定;最后,为了进一步提高相邻模态函数的正交性,对AEEMD分解结果进行二次处理。仿真试验验证了AEEMD方法的抗混分解能力,将AEEMD方法应用于转子油膜涡动的故障监测诊断中,提取出转子油膜涡动的故障特征,并与基本EMD算法进行了对比,结果表明,AEEMD更加精确地提取了油膜涡动信号的故障特征。
Ensemble empirical mode decomposition(EEMD) is a good method for suppressing the mode mixing in empirical mode decomposition(EMD),but its decomposition effect depends on two important parameters,which are the amplitude of added noise and the number of ensemble for EEMD.On the present time,these two important parameters mainly depend on experience and lack of reliability.In order to solve above problems,an improved algorithm named adaptive ensemble empirical mode decomposition(AEEMD) is proposed.Firstly,the amplitude standard deviation of original signal is computed;secondly,the original signal is decomposed by high-pass filters,and then the amplitude standard deviation of high frequency components is computed.Thus the amplitude standard deviation coefficient of added noise can be determined by the amplitude standard deviation of original signal and its high frequency components.Thirdly,the number of ensemble can be determined by the expectation AEEMD decomposition error and the amplitude standard deviation coefficient of added noise.Lastly,in order to decrease the mode mixing further and enhance the orthogonality of adjacent intrinsic mode function,an AEEMD post-processing method using EMD is proposed.The results of the simulation and experiments indicate that the proposed AEEMD algorithm can overcome mode mixing,and is successfully applied in the fault diagnosis of rotary machine,the result demonstrates that mode mixing can be controlled effectively,and then is compared with basic EMD algorithm.The results show that AEEMD is more precise,and it can exactly recognize rotary machine faults.

引文

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