摘要
针对压电柔性悬臂梁裂缝损伤检测与损伤程度识别问题,采用小波包分析和小波神经网络相结合的方法进行裂缝深度识别实验研究。利用小波包频带能量谱构造柔性悬臂梁裂缝损伤指标,即能量比相对变化量的H2范数,并建立压电柔性梁裂缝损伤实验装置。激励柔性梁的振动,记录两路压电传感器采集的振动信号,进行小波包分解并计算损伤指标。将这些损伤指标进行组合,作为小波神经网络的输入特征参数,进行裂缝深度即损伤程度的识别。实验结果表明:能量比相对变化量的H2范数对柔性梁的裂缝损伤敏感,对测试噪声不敏感;采用的小波神经网络可以精确识别柔性梁的裂缝深度。
In order to detect and recognize the extent of crack damage for piezoelectric flexible cantilever beam,experiments are conducted using the method of combining wavelet packet analysis with wavelet neural network(WNN).The damage index of H_2 norm of energy ratio relative variation is given based on wavelet packet energy spectrum.An experimental setup for cracked piezoelectric flexible beam is built up.After excitation,the vibration signals are measured by using two piezoelectric patches and decomposed by using wavelet packet to calculate damage indexes.The damage indexes are combined as input characteristic parameters of WNN to identify the depth of crack in the beam.The experimental results demonstrate that the H_2 norm of energy ratio relative variation is sensitive to crack damage of beam,and it is not sensitive to noise.The crack depth of the flexible beam can be recognized by using WNN accurately.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第3期393-398,523-524,共6页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51175181,60934001)
华南理工大学中央高校基本科研业务费专项资金资助项目(2012ZZ0060)
关键词
压电柔性梁
裂缝损伤识别
小波包能量谱
小波神经网络
piezoelectric flexible beam,crack damage identification,wavelet packet energy spectrum,wavelet neural network
