Smart Sensing for Structural Health Monitoring (S3HM)

用于结构健康监测的智能传感 (S3HM)

• 批准号: EP/E010849/1
• 负责人: Keith Worden
• 金额: $ 29.36万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE010849%2F1
• 关键词: Smart; Sensing; Structural; Health; Monitoring

The cost of unplanned maintenance for structures and systems is a significant issue in a number of fields of Engineering. For example, aircraft are critical structures which fall within the domain of Mechanical Engineering, bridges fall within the remit of Civil Engineering. A potential approach to reducing maintenance costs is to convert all unplanned maintenance to planned maintenance. In order to do this, one needs a means of assessing the health of a structure on a continuous basis. This is the domain of Structural Health Monitoring (SHM). The current project aims to develop SHM technologies based on the consideration of measured vibration data from structures. The idea is that the measured data will inform the Engineer of the current state of health. The approach taken will be based on advanced concepts of pattern recognition and machine learning. Algorithms will be developed which can learn the patterns in data which signal damage. A major problem in SHM is concerned with false indications of damage caused by the environmental conditions (e.g. the temperature) of the structure changing. A major part of this project will be concerned with the development of algorithms which can deal with the issues of environmental uncertainty.Another advantage of the availability of effective SHM is that one can adopt a damage tolerant approach to the design of the structure, this will give potential reductions in the design safety factors which will be passed on as reductions in manufacture cost.

结构和系统的计划外维护成本是许多工程领域的重要问题。例如，飞机是属于机械工程领域的关键结构，桥梁属于土木工程的范围。降低维护成本的一个潜在方法是将所有计划外维护转换为计划内维护。为了做到这一点，人们需要一种方法来评估一个结构的健康在一个连续的基础上。这就是结构健康监测（SHM）的领域。目前的项目旨在开发SHM技术的基础上考虑测量的振动数据的结构。其想法是，测量数据将告知工程师当前的健康状态。所采取的方法将基于模式识别和机器学习的先进概念。将开发算法，可以学习信号损坏的数据中的模式。SHM中的一个主要问题是由结构的环境条件（例如温度）变化引起的损伤的错误指示。该项目的一个主要部分将关注的算法的发展，可以处理的问题，环境的不确定性。有效的SHM的另一个优点是，可以采用损伤容限的方法来设计的结构，这将使潜在的减少设计的安全系数，这将通过减少制造成本。

项目成果

New trends in vibration based structural health monitoring

• DOI: 10.1007/978-3-7091-0399-9
• 发表时间: 2011
• 作者: A. Deraemaeker;K. Worden

Vibration-based structural health monitoring using large sensor networks

• DOI: 10.12989/sss.2010.6.3.335
• 发表时间: 2010-04
• 期刊: Smart Structures and Systems
• 影响因子: 3.5
• 作者: A. Deraemaeker;A. Preumont;E. Reynders;G. Roeck;J. Kullaa;V. Lamsa;K. Worden;G. Manson;R. Barthorpe;E. Papatheou;P. Kudela;W. Ostachowicz;P. Malinowski;T. Wandowski

On multi-site damage identification using single-site training data

• DOI: 10.1016/j.jsv.2017.07.038
• 发表时间: 2017-11-24
• 期刊: JOURNAL OF SOUND AND VIBRATION
• 影响因子: 4.7
• 作者: Barthorpe, R. J.;Manson, G.;Worden, K.
• 通讯作者: Worden, K.