Nonlinear System Identification for Real-Time Structural Health Monitoring in Composite Materials

用于复合材料实时结构健康监测的非线性系统识别

• 批准号: 9360372
• 负责人: Donald Sofge
• 金额: $ 6.5万
• 依托单位: NeuroDyne Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-01-15 至 1994-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9360372&HistoricalAwards=false
• 关键词: Nonlinear; System; Identification; Real; Time

9360372 Sofge Fiber/resin composite fabrication technologies new can produce high-strength, low weight structural components, while fiber-optic sensing technology provides a means for sensing various structural properties such as stress, strain, and elasticity when these sensors are mounted on, or embedded within, a material structure. Composite structures may degrade due to improper manufacture, hysteretic wear, impacts and corrosion. While embedded sensors provide a means for gathering vast quantities of data relating to the structural and dynamic history of composite structures, it is necessary to process, itegrate, and interpret this information if the embedded sensors are to be useful in determining structural integrity, using methods that can deal with the distributed and nonlinear nature of the problem. This research will utilize advance neural network based techniques for modeling and analyzing dynamic structural information, as well as neural network classifiers for performing real-time sensor interpretation. This will yield an intelligent system for processing , integrating, and interpreting embedded sensor data to give a real-time, robust, accurate measure of structural integrity for thick-section composite structures. This project involves cooperation with McDonnell Douglas based on earlier work with them in the area of embedded sensor signal processing and neural network based classification for composite structures. ***

小行星9360372 新的纤维/树脂复合材料制造技术可以生产高强度，低重量的结构部件，而光纤传感技术提供了一种用于传感各种结构特性的方法，例如当这些传感器安装在材料结构上或嵌入材料结构中时，应力，应变和弹性。 复合材料结构可能由于制造不当、滞后磨损、冲击和腐蚀而退化。 虽然嵌入式传感器提供了一种手段，用于收集大量的数据有关的结构和动态历史的复合材料结构，它是必要的处理，迭代，并解释这些信息，如果嵌入式传感器是有用的，在确定结构的完整性，使用的方法，可以处理的分布式和非线性性质的问题。 这项研究将利用先进的神经网络技术建模和分析动态结构信息，以及神经网络分类器进行实时传感器的解释。 这将产生一个智能系统，用于处理，集成和解释嵌入式传感器数据，以提供厚截面复合材料结构的结构完整性的实时，鲁棒，准确的测量。 该项目涉及与McDonnell道格拉斯的合作，基于他们在嵌入式传感器信号处理和基于神经网络的复合结构分类领域的早期工作。 ***