罗鹏, 陈嘉翊, 郭丽江等. 基于经验模态分解和改进小波软阈值降噪法的电站信号降噪研究. 力学与实践, 2023, 45(4): 736-743. DOI: 10.6052/1000-0879-22-593
引用本文: 罗鹏, 陈嘉翊, 郭丽江等. 基于经验模态分解和改进小波软阈值降噪法的电站信号降噪研究. 力学与实践, 2023, 45(4): 736-743. DOI: 10.6052/1000-0879-22-593
Luo Peng, Chen Jiayi, Guo Lijiang, et al. Study on de-noising of signal of power station based on empirical mode decomposition and modified wavelet soft-threshold de-noising method. Mechanics in Engineering, 2023, 45(4): 736-743. DOI: 10.6052/1000-0879-22-593
Citation: Luo Peng, Chen Jiayi, Guo Lijiang, et al. Study on de-noising of signal of power station based on empirical mode decomposition and modified wavelet soft-threshold de-noising method. Mechanics in Engineering, 2023, 45(4): 736-743. DOI: 10.6052/1000-0879-22-593

基于经验模态分解和改进小波软阈值降噪法的电站信号降噪研究

STUDY ON DE-NOISING OF SIGNAL OF POWER STATION BASED ON EMPIRICAL MODE DECOMPOSITION AND MODIFIED WAVELET SOFT-THRESHOLD DE-NOISING METHOD

  • 摘要: 在对电站各类信号进行故障诊断时,由于其所处的复杂工作环境,采集设备得到的信号中经常包含背景中强噪声干扰,导致在后续信号关键特征的识别和处理时会出现较大困难。为降低噪声的影响,本文将经验模态分解与改进小波软阈值降噪法相结合进行电站信号的处理并进行了方法有效性验证。该方法的主要流程如下:首先,将信号进行经验模态分解,分离出若干个本征模态分量和残差。其次,对其与原始信号进行互相关分析,识别有用信号主导的分量和噪声主导的分量,并将噪声信号主导的分量进行改进小波软阈值降噪法处理后,将其与有用信号分量和残差进行重构。最后,将经验模态分解和改进小波软阈值降噪法降噪处理后的信号与传统只采用小波阈值降噪法处理后的信号进行评价,结果表明本文方法的信噪比更高、均方根误差更小、相关系数更高、平滑度更好,可有效消除背景噪声的影响。

     

    Abstract: In the fault diagnosis of various signals of power station, due to the complex working environment, the signals obtained by acquisition equipments are often interfered by strong background noises, which brings great difficulties to the subsequent identification and processing of the key features of the signals. In order to reduce the influences of noises, this paper combines empirical mode decomposition (EMD) and the modified wavelet soft-threshold de-noising method to process the signal of power station and verifies the validity of the method. The main process of this method is given as follows. Firstly, the signal is decomposed by EMD in order to separate the several intrinsic mode functions (IMFs) and the residual part. Secondly, the cross-correlation analysis is carried out between each IMF and the original signal to identify IMFs dominated by useful components or by noise. And IMFs dominated by noise are reconstructed with IMFs dominated by useful components and the residual part after processed by the modified wavelet soft-threshold de-noising method. Finally, the de-noised signal based on EMD and the modified wavelet soft-threshold de-noising method is compared with the de-noised signal only based on the traditional wavelet threshold de-noising method. The comparison results show that the de-noised signal based on EMD and the modified wavelet soft-threshold de-noising method has a higher signal to noise ratio, a smaller root-mean-square error, a larger correlation coefficient and a better smoothness, which can effectively eliminate the influences of background noises.

     

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