基于小波变换和压缩感知对煤岩体声发射信号降噪方法

Acoustic Emission Signal Denoising Method for Coal and Rock Mass Based on Wavelet Transform and Compressed Sensing

  • 摘要: 针对煤岩体声发射信号具有随机非平稳、易受噪声信号干扰的特点,提出一种基于小波变换和压缩感知理论相结合的煤岩体声发射信号降噪方法.该方法利用噪声信号不具有稀疏性的特点,在小波分解的基础上进行压缩感知的压缩重构与降噪处理,使噪声信号与煤岩体声发射信号最大程度分离,该方法不仅达到降噪的目的,还避免了小波阈值滤波中阈值选取对降噪效果的影响.与小波阈值滤波、小波包降噪方法和压缩感知方法在选取不同压缩比、观测矩阵、重构算法对降噪效果进行对比.仿真结果表明,所提方法能够有效地抑制噪声信号的干扰,提高信号信噪比,降低均方误差,具有较强的实用价值.

     

    Abstract: We propose a noise reduction method for the acoustic emission of coal and rock based on wavelet transform and compressed sensing theory because the acoustic emission signals of coal and rock mass are random, non-stationary, and susceptible to noise signals. The proposed method utilizes the noise signal dispersion of the wavelet decomposition of the compression based on the perception of compression refactoring and noise reduction processing to ensure that the noise signal will be maximally separated from the coal and rock acoustic emission signal. The proposed method not only achieves noise reduction but also avoids threshold selection in wavelet filtering effect with respect tothe noise reduction effect. Further, different compression ratios, observation matrix, and reconstruction algorithm are used to compare the noise reduction effects of the wavelet threshold filtering, wavelet packet noise reduction method, and compression sensing method. The simulation results denotethat the proposed method can effectively suppress the interference of noise signals, improve the signal-to-noise ratio, and reduce the mean square error.

     

/

返回文章
返回