摘要
针对Mallat算法固有的频率混淆特性导致单子带重构算法提取出错误特征频率信息的问题,提出了一种能够消除频率混淆和边界效应的小波抗混叠单子带重构算法。该算法在信号的分解和重构过程中使用不同的纠正滤波器对各子带信号进行处理,同时在每一次卷积后加入去暂态过程点的步骤,可以克服Mallat算法和单子带重构算法中的频率混淆,保证了重构信号与原始信号的长度一致。对仿真信号的计算结果证明了该算法的有效性,为测量信号的特征信息提取分析提供了一种有效手段。
The frequency aliasing which rooted in the Mallat algorithm would lead to a wrong extraction of characteristic frequency in the single sub-band reconstruction algorithm, a completely anti-aliasing single sub-band reconstruction algorithm which could eliminate frequency aliasing and boundary effects is proposed in this paper. The algorithm uses different correcting filters to process each sub-band signal in the process of signal decomposition and reconstruction, at the same time, the steps of removing the transient process points are added after each convolution, which can overcome the frequency aliasing in the Mallat algorithm and the reconstruction algorithm, and the consistency of length between the reconstructed signal and the original signal is guaranteed. The simulation results show the effectiveness of the proposed algorithm, which provides an effective method for the feature information extraction and analysis of monitoring and measurement signals.
引文
[1] 孙宽雷,韩峻. 基于单子带重构改进算法的舰炮自动机故障特征提取方法[J]. 舰船电子工程, 2014, 34(10): 123-126
[2] Qin Y,Wang J X,Tang B P,et al. Higher Density Wavelet Frames with Symmetric Low-Pass and Band-Pass Filters[J]. Signal Processing,2010,90(12):3 219-3 231
[3] 杨建国. 小波分析及其工程应用[M].北京:机械工业出版社,2005
[4] 胡耀斌,谢静,胡良斌. 基于神经网络与小波变换的滚动轴承故障诊断[J].机械设计与研究,2013,29(6):33-35
[5] 李辉,丁桦. 一种抗混叠和失真的小波包信号分解与重构算法[J]. 科学技术与工程, 2008(20): 5 580-5 588
[6] 王林. 小波抗混叠单子带重构算法及其在轴承故障特征提取中的应用[D]. 重庆: 重庆大学, 2012
[7] 许珉,程兴民. 基于单子带重构改进小波变换的电力系统谐波检测方法[J]. 电力自动化设备,2008(9): 10-14
[8] 卢鑫,袁兴明. 基于M带小波的GPS信号特征提取探测[J].山东理工大学学报(自然科学版),2011,25(4):45-48
[9] 邵克勇,蔡苗苗,李鑫. 基于小波分析及奇异值差分谱理论的滚动轴承故障诊断[J]. 制造业自动化, 2013, 35(8): 79-82