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Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification

机译：概率神经网络与模糊聚类分析方法应用于基于阻抗的SHM损伤分类

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摘要

Impedance-based structural health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded on the surface of the structure (or embedded). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to quantify the damage. Distinguishing damage groups from a universe containing different types of damage is a major challenge in structural health monitoring. There are several types of failures that can occur in a given structure, such as cracks, fissures, loss of mechanical components (e.g., rivets), corrosion, and wear. It is important to characterize each type of damage from the impedance signals considered. In the present paper, probabilistic neural network and fuzzy cluster analysis methods are used for identification, localization, and classification of two types of damage, namely, cracks and rivet losses. The results show that probabilistic neural network and fuzzy cluster analysis methods are useful for identification, localization, and classification of these types of damage.
机译：通过测量由于存在损坏引起的结构的机电阻抗的变化来进行基于阻抗的结构健康监测技术。从结构（或嵌入）的表面上的压电材料块收集阻抗信号。通过这些压电陶瓷传感器致动器，获得与结构的机械阻抗直接相关的机电阻抗。基于阻抗信号的变化，可以检测损坏的存在。特定的伤害度量用于量化损坏。从包含不同类型损坏的宇宙中区分损伤群体是结构健康监测中的主要挑战。在给定的结构中有几种类型的故障，例如裂缝，裂缝，机械部件损失（例如，铆钉），腐蚀和磨损。重要的是要从考虑阻抗信号中表征每种类型的损坏。在本文中，概率神经网络和模糊聚类分析方法用于识别，本地化和两种类型的损坏，即裂缝和铆钉损失的分类。结果表明，概率神经网络和模糊聚类分析方法可用于识别，本地化和这些类型损坏的分类。

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