Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements
Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements
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DOI：
10.1117/12.2658628
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发表时间：
2023-04
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影响因子：
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通讯作者：
Yang Zhang;J. Dupont;Ting Wang;K. Zhou;Jiong Tang
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文献类型：
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作者：
Yang Zhang;J. Dupont;Ting Wang;K. Zhou;Jiong Tang
通过在参数空间中匹配测量值与模型预测值来识别构造中的故障参数。由于高频测量是发现小尺寸损伤的首选，压电阻抗/导纳主动探测显示出有希望的方面。然而，挑战依然存在。有用的测量信息通常不足以精确定位损伤。逆辨识通常是欠定的。在本研究中，我们开发了一种组合增强来解决这些挑战。提出了一种可调压电阻抗传感方法，该方法将自适应电感元件与压电传感器集成在一起，从而大大丰富了对相同损伤的测量数据。随后，综合了基于智能学习自动机的多目标粒子群优化框架，对损伤位置和严重程度进行了逆识别。案例研究强调了损害识别的准确性。
Fault parameters in a structure are identified by matching measurements with model predictions in the parametric space. As high frequency measurements are preferred to uncover small-sized damage, piezoelectric impedance/admittance active interrogation has shown promising aspects. Nevertheless, challenges remain. The amount of useful measurement information is generally insufficient to pinpoint damage. The inverse identification is usually underdetermined. In this research, we develop a combinatorial enhancement to tackle these challenges. A tunable piezoelectric impedance sensing procedure is developed in which an adaptive inductor element is integrated with the piezoelectric transducer, which will lead to significantly enriched measurement data for the same damage. Subsequently, an intelligent learning automata-based multi-objective particle swarm optimization framework is synthesized to inversely identify the damage location and severity. Case studies are conducted to highlight the accuracy of the damage identification.