巴特沃斯小波构造及其在故障诊断中的应用

• 作 者: 罗荣 田福庆 周菲 李万
• 机 构: 海军工程大学兵器工程系,中国人民解放军91872部队,
• 出 处: 华中科技大学学报(自然科学版)
• 发表日期: 2013年10期
• 关键词: 巴特沃斯 故障特征提取 包络解调 小波构造 故障诊断 小波包变换 Mallat算法 卷积型小波 小波滤波器 滤波器组

为消除Mallat算法存在频率混叠的缺陷,采用卷积型小波和小波包变换快速分解算法对信号进行分解.为克服Daubechise系列小波滤波器组幅频特性的不足,采用巴特沃斯滤波器构造了幅频特性更好的小波滤波器组,并导出了巴特沃斯小波.基于此,提出了一种结合卷积型小波和小波包变换快速分解算法与巴特沃斯小波的机械故障特征提取方法.仿真实验与实例分析表明:本方法比基于Daubechise系列小波与Mallat算法的机械故障特征提取方法在微弱故障特征提取方面更有优势.

In order to eliminate the shortcomings of the Mallat algos, we decomposed the signals by using the fast decomposition algorithm of convolutional wavelet transform and wavelet packet transform.In order to overcome the shortcomings of the amplitude-frequency characteristics of the Daubechise series wavelet filter banks, a Butterworth filter was used to construct A wavelet filter bank with better amplitude and frequency characteristics and a Butterworth wavelet are derived. Based on this, a fast fault decomposition algorithm based on convolutional wavelet transform and wavelet packet transform and a method for extracting mechanical fault features from Butterworth wavelet are proposed. Simulation experiments and case studies show that this method is superior to the mechanical fault feature extraction method based on Daubechise series wavelet and Mallat algorithm in the faint fault feature extraction.