针对超声水表在实际工作环境中容易受到噪声干扰从而导致计量精度下降的问题，提出了基于集合经验模态分解（EEMD）的改进小波阈值降噪算法。为了提高降噪效果，对小波阈值降噪算法进行了改进，构造了非线性阈值函数取代传统阈值函数，同时给出了一种分解尺度选择的方法。利用EEMD将流速信号分解为一系列的本征模态函数，通过改进小波阈值降噪算法对本征模态函数进行降噪处理，去除其中的噪声分量，为了验证该算法的适用性，将其与小波阈值降噪算法和时空滤波分析方法进行了比较。实验结果表明，以超声水表流速信号为降噪对象时，基于EEMD的改进小波阈值降噪算法具有较好的降噪效果。
Abstract
In light of the problem that ultrasonic water meters suffer the decrease of measurement accuracy due to susceptibility to noise interference in the actual working environment, an improved wavelet threshold noise reduction algorithm based on ensemble empirical mode decomposition (EEMD) is proposed. To improve the noise reduction effect, the wavelet threshold noise reduction algorithm is improved, a nonlinear threshold function is constructed to replace the traditional threshold function, and meanwhile, a method for selection of the decomposition scale is given. The flow rate signal is decomposed into a series of eigenmode functions using EEMD, and by means of an improved wavelet threshold noise reduction algorithm, the eigenmode function is subjected to noise suppression to remove the noise component. In order to verify the applicability of the algorithm, it is compared with the wavelet threshold noise reduction algorithm and the spatio-temporal filtering analysis method. The experimental results demonstrate that the improved wavelet threshold noise reduction algorithm based on EEMD has a better noise reduction effect on the flow rate signal of the ultrasonic water meter.
关键词
集合经验模态分解 /
超声水表 /
小波阈值降噪 /
小波变换 /
信噪比
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Key words
Ensemble empirical mode decomposition /
Ultrasonic water meter /
Wavelet threshold denoising /
Wavelet transform /
Signal-to-noise ratio
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