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首页> 外文会议>International workshop on structural health monitoring >Acoustic Emission Sensing using Mems for Structural Health Monitoring Demonstration: Of a Newly Designed Capacitive Micro Machined Ultrasonic Transducer
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Acoustic Emission Sensing using Mems for Structural Health Monitoring Demonstration: Of a Newly Designed Capacitive Micro Machined Ultrasonic Transducer

机译：使用Mems进行结构健康监测演示的声发射传感：一种新型设计的电容式微机械超声换能器

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Among the various experimental techniques used for Structural Health Monitoring (SHM), acoustic emission (AE) allows real time monitoring of large structures with the possibility to detect, characterize and locate damages. In practice, its implementation can be complex, especially for mobile structures. The main difficulties are related to the integration of sensors in the structure, electrical interconnections, different sources of noise, the detection and processing of transient signals and the management of massive data streaming. Generally, AE are collected through piezoelectric sensors. We propose an AE-based SHM methodology using an alternative technology in breakthrough with piezoelectric transduction: Capacitive micro-machined ultrasonic transducers (CMUTs). These sensors have many advantages. One advantage lies in the size of the sensors allowing a little intrusive integration into the material. Following a previous work published in EWSHM' 18, we report on the design, fabrication, and experimental demonstration of a new CMUT transducer specifically designed for the measurement of AE events. A data preprocessing methodology dedicated to interpret the obtained AE streaming is presented. We compare the results with standard piezoelectric sensors to detect damages during tensile tests on composite plates.
机译：在用于结构健康监测（SHM）的各种实验技术中，声发射（AE）可以对大型结构进行实时监测，并有可能检测，表征和定位损坏。实际上，它的实现可能很复杂，尤其是对于移动结构。主要困难涉及传感器在结构中的集成，电气互连，不同的噪声源，瞬态信号的检测和处理以及海量数据流的管理。通常，AE是通过压电传感器收集的。我们提出了一种基于AE的SHM方法，该方法使用了压电换能技术突破中的替代技术：电容式微加工超声换能器（CMUT）。这些传感器具有许多优点。优点之一在于传感器的尺寸，允许侵入性地集成到材料中。在EWSHM'18发表之前的工作之后，我们报告了专门用于测量AE事件的新型CMUT换能器的设计，制造和实验演示。提出了专用于解释所获得的AE流的数据预处理方法。我们将结果与标准压电传感器进行比较，以检测复合板上的拉伸试验中的损坏。

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