Smart Sensing for Structural Health Monitoring (S3HM)

结构和系统的计划外维护成本是工程学许多领域的一个重要问题。例如，飞机是属于机械工程领域的关键结构，桥梁属于土木工程领域。降低维护成本的一种潜在方法是将所有计划外维护转换为计划维护。为了做到这一点，人们需要一种持续评估结构健康状况的手段。这是结构健康监测(SHM)的领域。当前项目的目标是基于对结构测量振动数据的考虑来开发健康监测技术。这个想法是，测量的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。将开发能够学习数据中表示损坏的模式的算法。结构健康监测中的一个主要问题是由于结构的环境条件(如温度)的变化而造成的损害的错误指示。该项目的主要部分将致力于开发能够处理环境不确定性问题的算法。有效的结构健康管理的另一个好处是可以采用损伤容限方法进行结构设计，这将潜在地降低设计安全系数，这些安全系数将作为制造成本的降低而传递。

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.