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Automated fatigue crack detection for structural health monitoring of metallic aerospace structures

用于金属航空航天结构结构健康监测的自动疲劳裂纹检测

基本信息

批准号：
1943540
负责人：
金额：
--
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1943540
关键词：
Automated fatigue crack detection structural

项目摘要

Structural health monitoring (SHM) offers the ability to monitor structures, in real time, throughout their service lives to ensure they are safe for operation. Such technology has the potential to provide significant savings in maintenance and repair costs whilst increasing safety. For this reason there is currently a significant technology pull through for SHM techniques by the aerospace, energy, transport and infrastructure sectors. Acoustic emission (AE) is a passive SHM technique that is able to globally monitor large structures in real-time by detecting small amounts of energy that are released when damage grows in a structure. However, like many SHM techniques acoustic emission requires operator interpretation of data and can be less reliable in complex structures, which is seen as the greatest barrier to industrial implementation. This problem is made all the more challenging by the presence of uncertainty which results from measurement noise, lack of system knowledge, variability in operating conditions, etc. The applicant will develop excellent skills in signal processing, multi-variate statistics, novelty detection and uncertainty analysis. The aim of the work is to develop data processing methodologies that allow the automated detection of fatigue cracking in aluminium hangers. This will include techniques for accurate location of damage, the separation of extraneous noise from fracture signals, novelty detection and feature extraction. The developed techniques will be supported and validated by an extensive experimental fatigue testing programme, where the applicant will gain expertise in acoustic emission monitoring and experimental testing. The developed skills will also be widely applicable to the monitoring of a range of structures outside the initial aerospace focus. Supporting Documentation.

结构健康监测（SHM）提供了在结构的整个使用寿命中真实的实时监测结构的能力，以确保它们的安全运行。这种技术有可能大大节省维护和维修成本，同时提高安全性。因此，目前航空航天、能源、运输和基础设施部门对SHM技术有着重要的技术推动作用。声发射（AE）是一种被动SHM技术，能够通过检测结构中损伤增长时释放的少量能量来实时全局监测大型结构。然而，与许多SHM技术一样，声发射需要操作员对数据进行解释，并且在复杂结构中可能不太可靠，这被视为工业实施的最大障碍。由于测量噪声、缺乏系统知识、操作条件的变化等导致的不确定性的存在，使这个问题变得更加具有挑战性。申请人将在信号处理、多变量统计、新奇检测和不确定性分析方面发展出色的技能。这项工作的目的是开发数据处理方法，使铝衣架疲劳裂纹的自动检测。这将包括准确定位损伤、从断裂信号中分离外来噪声、新奇检测和特征提取的技术。开发的技术将得到广泛的实验疲劳测试方案的支持和验证，申请人将获得声发射监测和实验测试方面的专业知识。所开发的技能还将广泛适用于最初航天重点以外的一系列结构的监测。支持文件。