基于小波包分解法的超声信号特征提取方法研究

Research on Signal Feature Extraction Method Based on Wavelet Packet Decomposition

在超声金属缺陷检测过程中,超声信号采集系统收集到的信号波中存在着大量的信息,将信号中的特征提取出来是超声识别检测的关键,对于金属所处状态的评估也具有重要意义。为了更好地提取超声信号中的特征,本文采用小波包分解法对采集到的超声信号进行三层分解,提取最后一层的能量特征,组成能量特征向量。结果表明,小波包分析法可以很好地处理非线性、非平稳的超声,可广泛用于超声信号特征提取中。

In the process of ultrasonic metal defect detection, there is a lot of information in the signal wave collected by the ultrasonic signal acquisition system. It is the key of ultrasonic recognition and detection to extract the characteristics of the signal. It is also of great significance to evaluate the state of the metal. In order to better extract the features of ultrasonic signals, this paper adopts wavelet packet decomposition method to decompose the collected ultrasonic signals at three layers, and extracts the energy features of the last layer to form the energy feature vector. The results show that wavelet packet analysis can deal with nonlinear and non-stationary ultrasonic signals well, and can be widely used in signal feature extraction.