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用于推进系统结构健康监测的新型传感器和技术。

New sensors and techniques for the structural health monitoring of propulsion systems.

作者信息

Woike Mark, Abdul-Aziz Ali, Oza Nikunj, Matthews Bryan

机构信息

National Aeronautics and Space Administration, Glenn Research Center, Cleveland, OH 44135, USA.

出版信息

ScientificWorldJournal. 2013 Jul 11;2013:596506. doi: 10.1155/2013/596506. Print 2013.

DOI:10.1155/2013/596506
PMID:23935425
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3725820/
Abstract

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk.

摘要

监测旋转部件的结构健康状况,特别是涡轮发动机热段的结构健康状况,对于航空界提高发动机安全性和可靠性至关重要。在这些应用中使用仪器设备仍然极具挑战性。它需要高精度的传感器和技术,能够在高温环境下运行,并能在灾难性事件发生前检测到微小变化和隐藏缺陷。美国国家航空航天局(NASA)通过航空安全计划(AVSP),在开发用于燃气涡轮发动机原位结构健康监测的新型传感器技术和方法方面发挥了主导作用。本文概述了从三种不同的结构健康监测方法中获得的关键结果和发现。这包括评估一种新型微波叶片尖端间隙传感器的性能;一种使用外部安装的电容式叶片尖端间隙传感器的基于振动的裂纹检测技术;最后是使用数据驱动的异常检测算法检测旋转盘裂纹的结果。

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本文引用的文献

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Estimating the support of a high-dimensional distribution.估计高维分布的支撑集。
Neural Comput. 2001 Jul;13(7):1443-71. doi: 10.1162/089976601750264965.