结构关键部位疲劳裂纹检测技术研究

本文基于朴素贝叶斯分类方法，对lamb波信号的时频特征进行分类，从而监测结构关键部位的损伤状态。利用小波变换对压电传感元件接收到的Lamb波信号进行时间频率域分析，提取与损伤状态对应的损伤指标；在此基础上利用朴素贝叶斯分类器进行损伤状态的分类，得到结构关键部位的裂纹长度。典型结构关键部位（如接头耳片）的实验研究结果表明文中方法的有效性。

Fatigue crack detection technology for a structural hotspot

This work focuses on a data driven statistical approach to detect and monitor fatigue crack growth in lug joint samples using surface mounted piezoelectric sensors. Early and faithful detection of fatigue cracks in a lug joint can guide in taking preventive measures, thus avoiding any possible fatal structural failure. The on-line damage state at any given fatigue cycle is estimated using a damage index approach as the dynamical properties of a structure change with the initiation of a new crack or the growth of an existing crack. Using the measurements performed on an intact lug joint as baseline, damage indices are evaluated from the frequency response of the lug joint with an unknown damage state. As the damage indices are evaluated, a statistical metric is evaluated and a region selection metric is evaluated to identify the zone that the critical crack lies in. In the selected region, a Bayesian analysis is then committed to give the damage state of the selected zone.