S^3 Disease Surveillance for Structures and Systems

S^3 结构和系统疾病监测

• 批准号: EP/J016942/1
• 负责人: Keith Worden
• 金额: $ 113.55万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ016942%2F1
• 关键词: Disease; Surveillance; Structures; Systems

One of the main contributors towards the cost of high-value engineering assets is the cost of maintenance. Taking an aircraft out of service for inspection means loss of revenue. However, the alternative - allowing damage to remove the aircraft from service - is much more undesirable with cost and safety being issues. In terms of an offshore wind farm, the cost of an unscheduled visit to a remote site to potentially replace a 75m blade hardly bears thinking about. If one can adopt a condition-based approach to maintenance where the structure of interest is monitored constantly by permanent sensors and data processing algorithms alert the owner or user when damage is developing, one can optimise the maintenance program for cost without sacrificing safety. If incipient damage is detected, repair rather than replacement can be a viable option.Unfortunately, the complexity of modern structures together with the challenging environments in which they operate makes it very difficult to develop data-processing algorithms which can detect and identify incipient damage. The discipline concerned with these problems - structural health monitoring (SHM) - suffers from serious problems which have prevented uptake of the technology by industry. The structural complexity makes analysis difficult; however, one variant of SHM - the data-based approach - shows promise in this respect. In this case one learns directly from data from the structure using pattern recognition techniques to diagnose different levels of damage. Sadly, data-based SHM has its own problems; the first is that most pattern recognition approaches to SHM require one to measure data from the structure in all possible states of damage. In the case of a structure like an aircraft - consider the A380 - it is simply not conceivable that one should damage a single one for data collection purposes, let alone many. Fortunately, if one is only interested simply in whether damage is present or not, this can be accomplished using only data from the healthy condition. One builds a picture of the healthy state of the structure and then monitors for deviations from this state. This raises the second major issue with data-based SHM; if one is monitoring the structure for changes, one does not wish to raise an alarm because of a benign change in its environmental or operational conditions; these are termed 'confounding influences'.The solution may lie within the healthcare informatics community. A field called 'syndromic surveillance' (SS) has arisen over the last 20 years concerned with fast detection of disease outbreaks by monitoring human populations. The data themselves can be very different, from over-the-counter medicine sales to numbers of hits on health advice websites. The data are fused together and analysed to give a spatio-temporal picture of public health and alerting algorithms similar to the ones used for SHM can be used to warn healthcare professionals that an epidemic may be on the way. The ideas have even been embedded in software, the prime example being the ESSENCE II system which keeps a watchful eye over three US states.The current proposal aims to develop a SS system for engineering structures with the capability of fast detection and location for faults on high-value assets. The population-based approach to SHM proposed here has the potential to solve the two problems discussed above. If many structures are monitored, inferences between structures can potentially avoid the need for very detailed knowledge of individual structures. As structures fail with time, the knowledge of damage states will build. In terms of the second problem, SS systems have always dealt with confounding influences and can provide inspiration for new algorithms for data-based SHM. As in the case of ESSENCE II; the system will be embedded in software so that multiple operators of structures can derive maximum benefit from the diagnostic capability of the population-based system.

高价值工程资产成本的主要因素之一是维护成本。将飞机脱离服务以进行检查，意味着损失收入。但是，由于成本和安全性问题，替代方案 - 允许损坏以从服务中删除飞机。就一个离岸风电场而言，对偏远站点进行计划的费用可能更换了7500万叶片，几乎没有考虑。如果人们可以采用一种基于条件的方法来维护，在这种方法中，在损害发生损失时，通过永久传感器和数据处理算法不断监控感兴趣的结构，请提醒所有者或用户，则可以在不牺牲安全性的情况下优化维护程序的成本。如果检测到初期损害，则可以进行维修而不是更换。不幸的是，现代结构的复杂性以及它们操作的具有挑战性的环境，使得很难开发数据处理算法，这些算法可以检测和识别出发性损害。涉及这些问题的学科 - 结构性健康监测（SHM） - 严重问题，这阻止了行业吸收该技术。结构上的复杂性使分析变得困难；但是，SHM的一种变体 - 基于数据的方法 - 在这方面显示出希望。在这种情况下，人们使用模式识别技术直接从结构中学习，以诊断不同的损害水平。可悲的是，基于数据的SHM有自己的问题。首先是，大多数模式识别方法都需要一个人在所有可能的损害状态下从结构中测量数据。对于像飞机这样的结构 - 考虑A380-根本无法想象一个人应该损坏单个用于数据收集目的的人，更不用说许多。幸运的是，如果仅对是否存在损害感兴趣，那么只能使用来自健康状况的数据来完成这一点。一个人建立了结构的健康状态，然后监视与该状态的偏差。这引发了基于数据的SHM的第二个主要问题；如果一个人正在监视变更的结构，则由于其环境或操作条件的良性变化，人们不想发出警报。这些被称为“混淆影响”。该解决方案可能位于医疗保健信息学界。在过去的20年中，出现了一个称为“综合征监测”（SS）的领域，该领域与监测人群有关疾病暴发的快速检测。从非处方药销售到健康建议网站上的热门歌曲的数量，数据本身可能会大不相同。将数据融合在一起并进行分析，以提供公共卫生的时空图片，并提醒类似于SHM的算法，可以用来警告医疗保健专业人员，即流行病可能正在途中。这些想法甚至已经嵌入了软件中，一个典型的例子是Essence II系统，该系统对美国三个州保持警惕。目前的提案旨在开发用于工程结构的SS系统，具有快速检测能力和高价值资产故障的位置的能力。此处提出的基于人群的SHM方法有可能解决上面讨论的两个问题。如果监控了许多结构，则结构之间的推论可能会避免需要非常详细的个人结构知识。随着结构随着时间的流逝而失败，损害状态的知识将建立。就第二个问题而言，SS系统始终处理混杂的影响，并可以为基于数据的SHM的新算法提供灵感。与本质II一样；该系统将嵌入软件中，以便多个结构的操作员可以从基于人群的系统的诊断能力中获得最大的收益。

项目成果

Aspects of structural health and condition monitoring of offshore wind turbines.

• DOI: 10.1098/rsta.2014.0075
• 发表时间: 2015-02-28
• 期刊: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
• 作者: Antoniadou I;Dervilis N;Papatheou E;Maguire AE;Worden K
• 通讯作者: Worden K

Ontologies and Structural Health Monitoring.

本体论和结构健康监测。

• 作者: Antoniadou (I.)

Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo

• DOI: 10.3389/fbuil.2017.00052
• 发表时间: 2017-08
• 期刊: Frontiers in Built Environment
• 影响因子: 3
• 作者: A. B. Abdessalem;N. Dervilis;D. Wagg;K. Worden

A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions

• DOI: 10.1016/j.ymssp.2015.03.003
• 发表时间: 2015-12-01
• 期刊: MECHANICAL SYSTEMS AND SIGNAL PROCESSING
• 影响因子: 8.4
• 作者: Antoniadou, I.;Manson, G.;Worden, K.
• 通讯作者: Worden, K.

Cointegration for the Removal of Environmental and Operational Effects Using a Single Sensor

使用单个传感器消除环境和操作影响的协整

• 发表时间: 2013
• 期刊: STRUCTURAL HEALTH MONITORING 2013
• 作者: Antoniadou, I