摘要
针对现有各种降噪方法处理非平稳机械振动信号存在的缺点,提出一种基于辅助白噪声经验模式分解技术来自适应实现旋转机械非平稳振动信号降噪。该方法是一种集成的经验模式分解(Ensemble Empirical mode decomposition,EEMD)降噪算法,利用正态分布白噪声在经验模式分解中具有的二进尺度分解特性,可以有效抑制常规经验模式分解降噪算法处理非平稳振动信号时产生的模式混叠现象。通过仿真计算和转子启动过程试验振动信号对新降噪方法、经验模式分解降噪方法及小波降噪方法的性能进行了比较测试,结果表明,在非平稳机械振动信号降噪方面,新降噪方法具有更高的信噪比,不仅能够消除高斯噪声,而且能够有效降低脉冲干扰,提取出反映信号实际物理意义的振动固有模式。
Abstract
Focusing on the defects of different de-noising methods in processing non-stationary vibration signal, a method based on Gausses white noise assisted empirical mode decomposition (EMD) technique which adaptively eliminates noise involved rotating machine non- non-stationary vibration signal is presented. This method is an ensembled algorithm of EMD de-noising method, which applies dyadic scales decomposition characteristics of normal distribution white noise with EMD, could effectively suppress mode mixing that occurred in analyzing non-stationary vibration signal with EMD de-noising method and obtain higher signal-to-noise ratio(SNR). Simulation numerical signal and experimental signal of rotor running state are used to test and compare the performances of the method and EMD based de-noising method and wavelet de-noising method. The results show that the EEMD based noise cancellation method presented in the paper has more effective de-noising performance, not only eliminates random noise, but suppresses intensity noise and extracts vibration intrinsic modes that reflect real physical meaning of signal.
关键词
降噪 旋转机械 启动过程 振动信号 集成经验模式分解
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Key words
De-noising /
Rotating machine /
Running state /
Vibration signal /
Ensemble empirical mode decomposition /
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曹冲锋 杨世锡 杨将新.
大型旋转机械非平稳振动信号的EEMD降噪方法[J]. 振动与冲击, 2009, 28(9): 33-38
CAO Chongfeng YANG Shixi YANG Jiangxin.
DE-NOISING METHOD OF LARGE ROTATING MECHANICAL NON- STATIONARY VIBRATION SIGNAL BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION[J]. Journal of Vibration and Shock, 2009, 28(9): 33-38
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脚注
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