摘要
噪声消除是小波变换最成功的应用之一,其基本思想是将信号的小波变换系数通过阈值处理,然后进行小波重构得到降躁的信号。根据故障轴承声发射信号的脉冲特性选取Mor-let小波,以“小波熵最小”原则确定Morlet小波的波形参数,然后进行连续小波变换。采用软阈值方法处理小波系数,通过小波重构得到降噪后的故障声发射信号,噪声得到了很好的抑制,故障脉冲特征明显增强。采用实验数据,通过与离散小波变换的比较,得到了用连续小波变换可以有效降低噪声、提取故障声发射信号特征的结论。
Noise cancellation is one of the most successful applications of the wavelet transform. Its basic idea is to compare wavelet decomposition coefficients with the given thresholds anti only keep those bigger ones and then do wavelet reconstruction with them. In this paper, according to the impulse characteristic of bearing faults aeoustie emission signals, the Morlet wavelet is selected. The shape parameter of Morlet wavelet funetion is optimized based on the principle of minimal entropy. The denoised signals which were reconstructed by the wavelet coefficients fihered by the threshold value clearly showed the deteets characteristic frequency. Compared with the discrete wavelet transform, the continuous Morlet wavelet transform is fit for the denoising of bearing fauhs acoustic emission signals.
出处
《石家庄铁道学院学报》
2006年第4期34-37,51,共5页
Journal of Shijiazhuang Railway Institute
关键词
连续小波变换
噪声消除
滚动轴承
continuous wavelet transform
noise cancellation
rolling bearing
