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Synergetics based damage detection of frame structures using piezoceramic patches

机译:基于协同的压电陶瓷贴片损伤检测

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摘要

This paper investigates the Synergetics based Damage Detection Method (SDDM) for frame structures by using surface-bonded PZT (Lead Zirconate Titanate) patches. After analyzing the mechanism of pattern recognition from Synergetics, the operating framework with cooperation-competition-update process of SDDM was proposed. First, the dynamic identification equation of structural conditions was established and the adjoint vector (AV) set of original vector (OV) set was obtained by Generalized Inverse Matrix (GIM).Then, the order parameter equation and its evolution process were deduced through the strict mathematics ratiocination. Moreover, in order to complete online structural condition update feature, the iterative update algorithm was presented. Subsequently, the pathway in which SDDM was realized through the modified Synergetic Neural Network (SNN) was introduced and its assessment indices were confirmed. Finally, the experimental platform with a two-story frame structure was set up. The performances of the proposed methodology were tested for damage identifications by loosening various screw nuts group scenarios. The experiments were conducted in different damage degrees, the disturbance environment and the noisy environment, respectively. The results show the feasibility of SDDM using piezoceramic sensors and actuators, and demonstrate a strong ability of anti-disturbance and anti-noise in frame structure applications. This proposed approach can be extended to the similar structures for damage identification.
机译:本文研究了使用表面结合的PZT(铅酸锆钛酸盐)贴片对框架结构进行基于协同的损伤检测方法(SDDM)。在分析Synergetics模式识别机制的基础上,提出了SDDM协同竞争更新过程的操作框架。首先,建立结构条件的动态辨识方程,并通过广义逆矩阵(GIM)得到原始向量(OV)集的伴随向量(AV)集,然后通过以下公式推导阶数参数方程及其演化过程。严格的数学推理。此外,为了完成在线结构条件更新功能,提出了迭代更新算法。随后,介绍了通过改进的协同神经网络(SNN)实现SDDM的途径,并确定了其评估指标。最后,建立了一个两层框架结构的实验平台。通过松开各种螺丝螺母组方案,测试了所提出方法的性能以进行损坏识别。实验分别在不同的破坏度,扰动环境和噪声环境下进行。结果表明SDDM使用压电陶瓷传感器和执行器的可行性,并证明在框架结构应用中具有强大的抗干扰和抗噪声能力。可以将这种提议的方法扩展到类似的结构以进行损伤识别。

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