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Using the Random Decrement Method for the Decentralized Acquisition of Modal Data

机译:使用用于分散获取模态数据的随机递减方法

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Structural health monitoring methods based on modal properties are well suited for infrastructure objects, e.g. bridges or wind turbines. The size of these structures leads to a large number of sensors to be placed and long distances between them and the signal processing units. To save cabling effort, a communication via bus can be used. To realize this, the amount of data to be transmitted should be reduced by a network of smart sensors. This paper discusses a strategy for decentralized signal analysis with the Random Decrement method and Operational Modal Analysis. After a description of the Random Decrement method, the application in decentralized data acquisition is illustrated with a numerical example. Thereafter, a simple experimental structure exposed to wind loads is used to test the data acquisition in reality. By automating the process of modal decomposition, the described network is capable of acquiring the development of the eigenfrequencies and modeshapes over time. This is the input needed for structural health monitoring applications.
机译:基于模态特性的结构健康监测方法非常适合基础设施对象,例如基础设施物体。桥梁或风力涡轮机。这些结构的尺寸导致大量传感器放置和它们之间的长距离和信号处理单元。为了节省布线努力,可以使用通信通过总线。为了实现这一点,应该通过智能传感器网络减少要传输的数据量。本文讨论了随机减量方法和操作模态分析的分散信号分析策略。在对随机递减方法的描述之后,利用数字示例说明分散数据采集中的应用。此后,使用暴露于风力载荷的简单实验结构来测试现实中的数据采集。通过自动化模态分解过程,所描述的网络能够随着时间的推移获取特征频道和模划的开发。这是结构健康监测应用所需的输入。

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