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Hierarchical PSD damage detection methods for smart sensor networks

机译:智能传感器网络的分层PSD损伤检测方法

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摘要

Structural health monitoring(SHM)will transform the management and maintenance of civil infrastructure as available technology and methods continue to improve.Realizing the full potential of SHM requires the development of dense arrays of multi-scale sensors running robust and efficient programs.However,the scale of a densely instrumented civil infrastructure implies will generate vast amounts of data.Organizing the sensors in a hierarchical computing environment will store and process more data locally on each smart-sensor.This organization will greatly reduce the amount of data broadcast to a base station or multihopped along the network,thereby reducing its power consumption.However,because hierarchical computing only shares data locally,damage detection algorithms need to effectively perform in the presence of this constraint.This paper examines a damage detection algorithm that analyzes the changes in the structure’s Power Spectral Density(PSD)in a hierarchical,distributed computing environment.The method is modelindependent,requiring only output measurement.The effect of group size,sensor overlap,and frequency range are considered using numeric simulation.The results show that limitations exist for these variables to maintain the functionality of the damage detection algorithm.Nevertheless,when the hierarchical distribution properly addresses these limitations,the proposed algorithm is effective in accurately detecting damage.
机译:随着可用技术和方法的不断改进,结构健康监测(SHM)将改变民用基础设施的管理和维护。要发挥SHM的全部潜能,就需要开发密集阵列的多尺度传感器,这些传感器应运行可靠且高效的程序。密集的民用基础设施的规模意味着将生成大量数据。在分层计算环境中组织传感器将在每个智能传感器上本地存储和处理更多数据。该组织将大大减少广播到基站的数据量但是,由于分层计算仅在本地共享数据,因此损坏检测算法需要在存在此约束的情况下有效地执行。本文研究了一种损坏检测算法,该算法分析了结构的变化。分层分布式计算中的功率谱密度(PSD)在环境中。该方法与模型无关,仅需要输出测量。使用数值模拟考虑了组大小,传感器重叠和频率范围的影响。结果表明,这些变量存在限制,以保持损伤检测算法的功能。然而,当分层分布适当地解决了这些限制时,所提出的算法对于准确地检测损坏是有效的。

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