基于机电阻抗法的桁架结构加载状况监测

Loading Condition Monitoring of Truss Structure Based on Electromechanical Impedance Method

采用机电阻抗法监测三跨桁架结构加载状况,并利用BP神经网络方法进行结构载荷的定位和定量研究.首先,研究激励频率对压电陶瓷片感知结构变化的灵敏度的影响,并选取最佳敏感频段为190~200 k Hz.然后,测得2个监测节点独立性良好,有助于实现载荷定位监测.最后,将采集到的桁架结构加载时的部分阻抗虚部数据进行合理的数据压缩后作为输入样本.压缩前后的数据对比显示这种压缩方法具有可靠性.建立并训练神经网络,剩余部分数据经过相同处理后作为测试样本对训练好的BP网络进行测试.实验结果表明：基于机电阻抗法,利用神经网络可有效实现桁架结构中载荷的精确定位与定量.

Loading condition of a three-span truss structure was monitored by using electromechanicalimpedance method and BP neural network was used for the localization and quantification of structural loading. First, the effect of excitation frequency on the impedance of PZT element was studied and the most sensitive frequency band of 190-200 kHz was selected. Second, the independence of the two monitoring nodes was tested so well and this is helpful for loading positioning. Finally, when the truss structure was loaded, theimaginary part ofimpedance data that were compressed reasonably was collected and parts of these were regarded as the input samples of the neural network. The comparison of the data before and after compressionillustrates the reliability of this data compression method. The rest parts after the same process were served as the training samples to test the trained network. Results show that the trained neural network has excellent abilities of recognition and classification of information that can also accurately locate and quantify the load in the truss structure.