声发射和小波包分析在损伤状态监测中的应用  

Combined Application of Acoustic Emission and Wavelet Packet Analysis on Damage Condition Monitoring of Structures

作　　者：操礼林[1,2] 李爱群[2] 邓扬[2] 丁幼亮[2] 

机构地区：[1]江苏大学土木工程系,镇江212013 [2]东南大学土木工程学院,南京210096

出　　处：《振动．测试与诊断》2012年第4期591-595,688-689,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家杰出青年基金资助项目(编号:50725828);国家自然科学基金资助项目(编号:50808041)

摘　　要：为应用声发射技术对结构的损伤状态进行监测与识别,基于小波包分析提出了一种在线损伤监测方法。采用D-S证据理论对声发射信号的小波包能量谱进行多源信息融合得到改进的小波包能量谱,计算Mahalanobis距离构建结构损伤状态判别指标,采用t检验的概率方法考察不同损伤状态下判别指标的变化情况。旋转轴承结构的声发射信号分析结果表明,D-S证据理论能够对声发射信号的小波包能量谱识别样本抽取有效信息,结构损伤状态判别指标具有良好的结构损伤识别能力,能够准确地实现对结构不同损伤状态的识别。In order to apply the technique of acoustic emission to monitor and identify the damage condition of structures on line, a damage monitoring method is presented using wavelet packet analysis. The wavelet packet energy spectrum identified under the wavelet packet analysis from the measurement of acoustic emission are fused using D-S evidence theory to obtain improved wavelet packet energy spectrum, on this basis the damage condition discrimination index DCDI is proposed using mahalanobis distance. Then a hypothesis test involving t-test method is carried out to investigate the change of the index DCDI of different damage conditions. The analysis results of the acoustic emission signals of swivel bearings reveal that: the effective information can be extracted from the testing samples of wavelet packet energy spectrum from the measurement of acoustic emission using D-S evidence theory, the damage condition discrimination index DCDI has preferable capacity of damage identifying and can identify the different damage condition of structures accurately.

关 键 词：损伤状态监测 声发射 小波包分析 小波包能量谱 信息融合 

分 类 号：TU311.41[建筑科学—结构工程]