将压电传感与主动Lamb波监测技术相结合，研究在静拉伸加载状态下碳纤维复合材料T型接头（T700／BA9916）界面脱粘及扩展过程中的信号特征，并采用改进后的BP神经网络系统对接头损伤状态进行识别。实验结果表明：T型接头脱粘首先发生在三角填充区，后向突缘扩展；接头失效前，信号能量和最小二乘峰值因子随时间呈线性递减，能够表征脱粘程度，利用自适应微粒群算法改进后的网络训练值与实验观测值之间的误差为3．8％～4．7％。
The signal features of CFRP T- joint disbond under static extension testing were investigated by piezoelectric sensors and active Lamb wave monitoring technology, and disbond damages were identified with improved BP artificial neural networks. The experimental results show that interfacial disbond appears firstly in T- joint triangle filling area and then extend to flanges. Both of the signal energy and least square peak factor linearly decrease with time before failure, which can be used to describe disbond extension of T-joint. The network training data improved by adaptive particle swarm optimization algorithm are corresponded to experimental results with error range of 3.8%-4.7%.
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