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片上系统集成的一种用于飞机结构健康监测的新型机电阻抗计算方法。

System-on-chip integration of a new electromechanical impedance calculation method for aircraft structure health monitoring.

机构信息

Le Laboratoire d'Analyse et d'Architecture des Systèmes, French National Center for Scientific Research, 7 Avenue du Colonel Roche, F-31077 Toulouse, France.

出版信息

Sensors (Basel). 2012 Oct 11;12(10):13617-35. doi: 10.3390/s121013617.

Abstract

The work reported on this paper describes a new methodology implementation for active structural health monitoring of recent aircraft parts made from carbon-fiber-reinforced polymer. This diagnosis is based on a new embedded method that is capable of measuring the local high frequency impedance spectrum of the structure through the calculation of the electro-mechanical impedance of a piezoelectric patch pasted non-permanently onto its surface. This paper involves both the laboratory based E/M impedance method development, its implementation into a CPU with limited resources as well as a comparison with experimental testing data needed to demonstrate the feasibility of flaw detection on composite materials and answer the question of the method reliability. The different development steps are presented and the integration issues are discussed. Furthermore, we present the unique advantages that the reconfigurable electronics through System-on-Chip (SoC) technology brings to the system scaling and flexibility. At the end of this article, we demonstrate the capability of a basic network of sensors mounted onto a real composite aircraft part specimen to capture its local impedance spectrum signature and to diagnosis different delamination sizes using a comparison with a baseline.

摘要

本文介绍了一种用于碳纤维增强聚合物制成的新型飞机部件主动结构健康监测的新方法实现。该诊断基于一种新的嵌入式方法,通过计算粘贴在其表面上的压电片的机电阻抗,能够测量结构的局部高频阻抗谱。本文涉及基于实验室的 E/M 阻抗方法的开发,将其集成到具有有限资源的 CPU 中,以及与实验测试数据的比较,以证明复合材料缺陷检测的可行性,并回答方法可靠性的问题。本文介绍了不同的开发步骤,并讨论了集成问题。此外,我们还介绍了通过片上系统 (SoC) 技术实现可重构电子带来的系统扩展和灵活性的独特优势。最后,我们展示了安装在真实复合材料飞机部件样本上的基本传感器网络的功能,该网络能够捕获其局部阻抗谱特征,并通过与基线比较来诊断不同的分层大小。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3993/3545584/4a1242c77db0/sensors-12-13617f1.jpg

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