基于振动响应内积向量和数据融合的结构损伤检测方法试验研究

提出了一种基于振动响应内积向量(Inner Product Vector, IPV)和数据融合的损伤检测方法，并进行了相应的验证试验研究。分别以随机信号和正弦信号对结构进行激励，利用加速度传感器采集结构的振动响应信号，计算结构的损伤指标，然后利用数据融合理论将结构各参考点下的损伤指标进行融合。损伤检测的验证试验结果表明，结合数据融合理论后，能够避免原始IPV方法中参考点选取对检测准确度的影响问题，并能准确进行损伤定位。两组不同激振信号的检测结果对比显示，数据融合对外激励为正弦信号的检测结果的准确度提升更为显著。

Damage detection of structures by a combined approach of IPV method and data fusion

Structural damage detection method using the inner product vector (IPV) of vibration response and data fusion was investigated in this paper, and the corresponding damage detection experiments were performed. In the experiments, random excitation and sinusoidal excitation were adopted, respectively，and the acceleration responses of the structure were acquired. The damage indexes were calculated based on IPV technique, and then the data fusion technique was further performed on the damage indexes calculated by different reference measuring points. The damage detection experiment of a frame structure showed that the misjudgment of damage caused by reference point selection in the original IPV method can be avoided and the structural damage can be detected correctly when combining the IPV method with data fusion technique. The damage detection results comparisons under these two different type excitations, demonstrates that data fusion technique can improve the damage detection precision significantly when the structure is excited by sinusoidal signal.