小波包在声发射信号特征提取中的应用

Application of Wavelet Packet in Feature Extraction of Acoustic Emission Signal

在分析典型声发射(AE)信号特征的基础上,根据机械故障或损伤引发的AE信号的特征提取原理,首次提出AE信号的小波包特征提取分析法。该方法利用小波包将信号按任意时频分辨率分解到不同频段的特点,可从AE信号中提取和重构出所需的特定时频段(点)上的特征信号,解决了不能从噪声大、频带宽和数据量大的实测AE信号中有效提取特征信号的难题。介绍了该方法的具体算法,并通过仿真研究了该方法在强噪声背景下提取特征信号的能力;将其用于声发射检测的滚动轴承损伤类型及部件的识别,诊断结果十分清晰、可靠和精确。仿真和实验研究均表明了AE信号的小波包特征提取分析法能有效应用于基于声发射技术的状态监测和故障诊断。

Based on analyzing the typical characteristics of acoustic emission （AE） signals, and according to ent frequency channels with random time-frequency resolution, and extract and reconfigurate the specific time-frequency channel （dot） signals from AE signals. The proposed method resolves the problems that AE signals of actual measurement have characteristics of strong noise, broadband and big data size, and it is difficult to extract characteristic signals effectively. The algorithm of the method is introduced. The capability of extracting characteristic signal under strong noise background is studied using simulation. The method was applied in the fault diagnosis of rolling bearings. The diagnosis results are quite clear, reliable and accurate. Both simulations and experimental researches prove that the proposed method can be used for condition monitoring and fault diagnosis based on AE detection technique.