ABSTRACT The current state of infrastructure in the United States and worldwide has raised the need for reliable structural health monitoring techniques. Piezoelectric sensing, such as acoustic emission, has recently gained attention due to its high sensitivity and associated capability for early detection of damage. The high sensitivity of this method, however, results in the collection of data not directly related to damage growth. Current filtering procedures focus primarily on parametric analysis of the collected signals. This study focuses on developing more robust filtering techniques for acoustic emission data collected from a prestressed concrete specimen. Simulated data was generated to enable proper identification of the source of the collected signals. Filtering criteria were developed through characterization of the energy content using a wavelet transform. The developed filters were capable of separating the induced target signals from other signals with reasonable accuracy, and the results were verified through source location. The developed filters were validated using acoustic emission data collected during a load test.

中文翻译：

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过滤预应力混凝土声发射数据的信号处理技术

摘要 美国和世界范围内基础设施的现状已经提出了对可靠的结构健康监测技术的需求。压电传感，例如声发射，由于其高灵敏度和相关的早期损伤检测能力而最近受到关注。然而，这种方法的高灵敏度导致收集的数据与损伤增长没有直接关系。当前的过滤程序主要集中在收集信号的参数分析上。本研究的重点是为从预应力混凝土试样收集的声发射数据开发更强大的过滤技术。生成模拟数据以正确识别收集到的信号的来源。过滤标准是通过使用小波变换表征能量含量而开发的。开发的滤波器能够以合理的精度将感应目标信号与其他信号分离，并通过源位置验证结果。使用负载测试期间收集的声发射数据对开发的过滤器进行了验证。