基于参数优化Morlet小波变换的故障特征提取方法

Feature extraction method based on parameter optimized Morlet wavelet transform

针对轴承或齿轮箱等机械元件的故障振动信号表现为冲击衰减波形的特点,提出一种基于参数优化Morlet小波变换的故障特征提取方法。利用最小Shannon熵方法优化Morlet小波的形状参数,实现与冲击特征成分的最佳匹配,再对小波变换系数矩阵进行奇异值分解,根据奇异值曲线中主要反映突变信息的过渡阶段所对应的尺度范围求得最佳小波变换尺度,最后对信号进行Morlet小波变换提取故障特征。仿真试验和实际应用的结果表明,该方法能更有效地从强噪背景中提取故障特征。

According to the fact that the defects of mechanical components such as rolling beating or gearbox can excites vibration with specific impact component, a feature extraction method based on parameter optimized Morlet wavelet is proposed. Firstly, minimum Shannon entropy is used to optimize the Morlet wavelet shape factor in order to match with the impact component. Then, an abrupt information detection method based on the transitional stage of singular curve of wavelet coefficient matrix is used to choose the appropriate scale for the wavelet transformation. Finally, the fault feature of the signal can be extracted using this method. The experimental results and analysis resuits of rolling bearing signals show that the proposed method could extract fault feature more effectively.