【摘要】 经典小波变换在不同尺度采用一种小波基 ,不能很好地匹配信号的局部特征 ,因而造成降噪信号丢失了原始信号中部分的有用信息。为了克服上述缺陷 ,提出了一种基于第二代小波变换的非线性小波变换振动信号预处理方法 .应用第二代小波变换的预测器和更新器相互独立的特点 ,根据预测方差最小的选取原则 ,确定每个变换样本的最佳预测器 ,使预测器能够适应信号的局部特征。模拟数据和振动信号的分析表明 ,该方法克服了传统小波降噪方法局部信息丢失的缺陷 ,不仅可以有效地去除信号中的噪声 ,而且能够保留信号的局部特征。作为一种预处理方法 ,在某发电厂的故障诊断中有效地从振动信号中提取了故障特征。更多还原

【Abstract】 Because one wavelet basis is adopted at every level in classical wavel et transform that can not ideally match the local characteristics of signals, so me useful information of original signals is lost in denoised signals. In order to overcome the mentioned limitation, a pre-processing method based on nonlinea r wav elet transform for vibration signals is adopted by using second generation wavel et transform (SGWT). By virtue of the property of predictor and updater independ ent each other in SGWT, an optimal predictor is selected for a transforming samp le according to the selection criterion of minimizing the squared error for pred iction. Consequently, the selected predictor can always fit the local characteri stics of the signals. The simulations showed tha t the proposed method could overcoame the disadvantage of classical denosing app r oach that lose local information of original signals. It not only can filter noi se from original signals effectively, but also can hold local characteristics of original signals in the deniosed signals. Fault features were successfully extr acted from vibration signal for further diagnosis in a power plant by taking the proposed method as a preprocessing tool.更多还原