论文部分内容阅读
在CO2 弧焊过程电信号的实际测试过程中 ,噪声的存在常是难以避免的 ,消除噪声是信号分析中重要的环节。小波变换具有很好的时、频局域性 ,以不同的小波变换尺度 ,可将信号分解成不同的频率分量。对于连续信号函数 ,随着尺度的增大 ,小波变换系数也增大 ;对于噪声 ,其小波变换系数随尺度的增大而减小。据此 ,可消除信号中的噪声成分。论文重点分析了小波软阈值法信号消噪方法 ,通过对实测弧焊过程电信号的消噪处理 ,对比分析了低通滤波、粗糙小波和小波软阈值消噪法。结果表明 ,采用低通滤波和粗糙小波消噪 ,会在信号的突变部分产生严重的失真。而小波软阈值法可以在消去信号噪声的同时 ,较好地保持信号的突变部分不失真 ,从而改善信号中特征信息的提取效果。
During the actual test of electric signal in CO2 arc welding process, the existence of noise is often unavoidable. Eliminating noise is an important link in signal analysis. Wavelet transform has good time-frequency localization, with different wavelet transform scales, the signal can be decomposed into different frequency components. For the continuous signal function, as the scale increases, the wavelet transform coefficient also increases; for the noise, the wavelet transform coefficient decreases with the increase of scale. Accordingly, noise components in the signal can be eliminated. The paper focuses on the analysis of the wavelet denoising method based on the soft-thresholding method. By comparing with the de-noising processing of the electrical signal in the actual arc-welding process, the low-pass filtering, the rough wavelet and the wavelet soft threshold denoising method are comparatively analyzed. The results show that the use of low-pass filtering and rough wavelet denoising can cause serious distortion in the abrupt part of the signal. The wavelet soft thresholding method can eliminate the signal noise and keep the abrupt part of the signal well without distortion, so as to improve the extraction effect of the feature information in the signal.