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Physics-informed data-driven loose bolt identification

基于物理的数据驱动的松动螺栓识别

基本信息

批准号：
2759856
负责人：
金额：
--
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2759856
关键词：
Physics informed data driven loose

项目摘要

Maintenance of large structures is an extremely costly and often hazardous endeavour which needs to be performed on a regular basis. Among the many inspection tasks which needs to be undertaken, the identification of loose bolts is an especially daunting one, in particular when this operation concerns large structures located in poorly accessible or unsafe environments (offshore platforms, bridges, rail tracks, etc.). At present there are no commercially-available technologies which enable to perform loose bolt identification in a reliable, safe and fast manner, thus requiring a one-by-one measurement of the screwing torque of each bolt. A newly patented technique filed by the Japanese partners of this project uses laser ablation to trigger high frequency vibrations in large portion of a structure. By studying these high frequencies vibrational responses, it is possible to identify which bolt have come loose. This technique has so far been successfully demonstrated in the case of a small-scale metallic plate with a limited number of bolts. Upscaling this protocol to large-scale structures requires interpreting the subtle changes in the vibrational modes from a default state. This project aims to develop a machine learning technique which analyses the pre and post modal frequency response of a generic, large-scale structure from laser ablation testing and uses this data to accurately identify the loose bolts.

大型建筑物的维修是一项极其昂贵且往往危险的工作，需要定期进行。在需要进行的许多检查任务中，确定松动螺栓是一项特别艰巨的任务，特别是当这项工作涉及位于难以接近或不安全的环境(海上平台、桥梁、铁轨等)的大型结构物时。目前还没有商业上可用的技术能够以可靠、安全和快速的方式进行松动螺栓识别，因此需要对每个螺栓的拧紧扭矩进行逐个测量。该项目的日本合作伙伴申请了一项新的专利技术，该技术使用激光烧蚀在结构的大部分引发高频振动。通过研究这些高频振动响应，可以确定哪个螺栓松动了。到目前为止，这项技术已经在具有有限数量螺栓的小型金属板的情况下得到了成功的演示。将这一协议扩展到大规模结构需要解释从默认状态到振动模式的细微变化。本项目旨在开发一种机器学习技术，通过分析激光烧蚀测试中的通用大型结构的振型前和振型后的频率响应，并使用这些数据来准确识别松动的螺栓。