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为了能在强噪声背景下准确地进行振动信号的特征提取,对经验模式分解进行了研究和改进,并将其应用于车辆振动信号的特征提取中。首先对系统中各输入信号进行了多次自相关处理,有效地降低信号中的噪声。然后对处理的信号进行经验模式分解,得到了各固有模态函数分量。最后对感兴趣的固有模态函数分量进行希尔伯特变换和谱分析,从而得到信号的特征信息。仿真和试验分析说明了改进的经验模式分解方法的可行性,并且对同类工程问题具有一定的参考价值。
In order to extract the feature of the vibration signal accurately in the background of strong noise, the empirical mode decomposition is studied and improved, and applied to the feature extraction of vehicle vibration signal. First of all, the input signal in the system for a number of autocorrelation, effectively reduce the signal noise. Then, the processed signals are decomposed empirically, and the intrinsic mode function components are obtained. Finally, the Hilbert transform and spectral analysis of the intrinsic mode function components of interest are obtained, and the characteristic information of the signal is obtained. Simulation and experimental analysis demonstrate the feasibility of the improved empirical mode decomposition method, and have some reference value for similar engineering problems.