冗余第2代小波构造及机械信号特征提取

Construction of Redundant Second Generation Wavelet and Mechanical Signal Feature Extraction

针对强噪声背景下机械信号故障特征的提取问题,构造了一种提取该类信号时域特征的冗余第2代小波方法.该方法通过对初始预测算子和更新算子插值补0,来获得不同分解层上的预测算子和更新算子.冗余第2代小波不需要剖分运算,直接利用构造的算子对逼近信号进行对称预测和更新,可使逼近信号和细节信号的数据点数保持不变,并根据每层细节信号的噪声特点选取降噪阈值门限.实验和工程振动信号分析表明,冗余第2代小波的降噪效果优于其他类型的小波方法,较理想地提取出了滚动轴承内圈剥落和汽轮发电机组高压缸蒸汽激振的时域故障特征.

To extract fault feature of mechanical signal corrupted by noise, a new method to construct redundant second generation wavelet (RSGW) is presented to extract fault feature in time domain. The initial prediction operator and initial update operator are interpolated with zero, and then the prediction operator and update operator at different decomposition scale are gained. Splitting operation is unnecessary for RSGW, and the approximation signal is predicted and updated directly, the length of approximation signal and detail signal for all scales remains identical, and the thresholds at different scales are selected according to the noise characteristics. Experimental and engineering results confirm the advantages of RSGW over the other wavelet methods for signal de-noising, and the fault features of bearing inner raceway damage and vibration excited by steam in high-pressure turbine of turbo-generator in time domain are desirably extracted by RSGW.