Structural Health Monitoring of Systems of Systems: Populations, Networks and Communities

高价值工程资产成本的主要贡献者之一是维护成本。让一架飞机停止服役进行检查意味着损失收入。然而，如果损坏发生并导致灾难性故障，安全和人员伤亡是主要问题。就海上风电场而言，不定期地前往偏远的海洋地点更换75米长的叶片的成本非常高。如果采用一种维护方法，即通过永久性传感器不断监测感兴趣的结构，并且数据处理算法在损坏发生时提醒所有者或用户，则可以在不牺牲安全性的情况下优化维护计划以降低成本。如果及早发现损坏，维修而不是更换是可行的。现代结构的复杂性及其具有挑战性的操作环境使得开发能够检测和识别早期损伤的算法变得困难。相关学科——结构健康监测（SHM）——存在阻碍工业采用该技术的问题。尽管结构的复杂性使分析变得困难，但SHM的一种变体——基于数据的方法——显示出很大的希望。在这种情况下，人们使用机器学习技术从测量数据中诊断损坏。基于数据的SHM面临许多挑战；首先，大多数基于数据的SHM方法需要结构在所有可能的损伤状态下的测量数据。对于像5兆瓦风力涡轮机这样的结构来说，为了收集数据而损坏一个是不可想象的，更不用说很多了。幸运的是，如果一个人只对是否存在损害感兴趣，这是可能的，只使用健康状况的数据。一个是构建结构健康状态的图像，然后监测偏差。这就提出了基于数据的SHM的第二个问题；如果一个人正在监测结构的变化，他不希望被环境/操作条件的良性变化所欺骗，即所谓的“混杂影响”。最初的奖学金旨在通过以“综合征监测”（SS）学科为模型的基于人群的SHM方法来解决这些问题，SS用于检测人群中的疾病暴发。提出的研究的核心是一个智能数据库，包含不同结构群体的数据，以及一个推理引擎，可以使用来自个体的损伤数据，从而对其他人进行诊断。原来的工作进展得很好；创建了所需的数据库，开发并演示了跨种群推断的算法。消除混淆影响的算法也被创造出来，这可以说是目前最先进的。到目前为止，该项目还让人们深入了解了基于人口的SHM如何能够远远超越基于SS的技术，从而产生了这个新提议。将探索SHM中非常新的概念。第一个想法是将“数据库”扩展为“本体”；本体编码、共享和重用领域知识。在某种程度上，转向本体为现有的存储和处理增加了一个“语言中心”；人们甚至可以把这个结果想象成一个专注于特定工程领域的计算大脑——在这个例子中是SHM。提出了新的基于人口的方法。对于几乎相同结构的种群，提出了结构“形式”的概念。该表格是用来表示群体中的所有个体的，如果风力发电场中单个涡轮机的损坏数据可用，则可以将其转移到表格中，从而允许对整个农场进行推断。此外，将使用数学和计算中的思想构建不同结构总体的一般理论：几何，图论，复杂网络和机器学习。再一次，该理论将允许个体的损害数据来产生对整个群体的见解。

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, if damage occurs and leads to catastrophic failure, safety and casualties are major issues. In terms of an offshore wind farm, the cost of an unscheduled visit to a remote ocean site to replace a 75m blade is exceedingly high. If one adopts an 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 programme for cost without sacrificing safety. If damage is detected early, repair rather than replacement can be viable.The complexity of modern structures and their challenging operating environments make it difficult to develop algorithms that can detect and identify early damage. The relevant discipline - structural health monitoring (SHM) - suffers from problems that have prevented uptake of the technology by industry. Although structural complexity makes analysis difficult, one variant of SHM - the data-based approach - shows great promise. In this case one uses machine learning techniques to diagnose damage from measured data. Data-based SHM faces a number of challenges; the first is that most data-based approaches to SHM require measured data from the structure in all possible states of damage. For a structure like an 5 MW wind turbine - 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 in whether damage is present or not, this is possible using only data from the healthy condition. One builds a picture of the healthy state of the structure and then monitors for deviations. This raises a second issue with data-based SHM; if one is monitoring the structure for changes, one does not wish to be deceived by a benign change in its environmental/operational conditions - so-called 'confounding influences'.The original Fellowship aimed to solve these problems via a population-based approach to SHM modelled on the discipline of 'syndromic surveillance' (SS), which is used to detect disease outbreaks in human populations. The core of the proposed research was an intelligent database holding data across populations of structures, and an inference engine that could use damage data from an individual, to allow diagnostics on others. The original work has progressed very well; the required database was created and algorithms for inference across populations have been developed and demonstrated. Algorithms for removing confounding influences have also been created which are arguably now the state of the art. The Fellowship so far has also allowed insights into how population-based SHM can go far beyond technologies based on SS, leading to this new proposal. Very new concepts in SHM will be explored. The first idea is to extend the 'database' to an 'ontology'; ontologies encode, share and re-use domain knowledge. In a way, moving to an ontology adds a 'language centre' to the existing storage and processing; one might even think of the result as a computational brain concentrating on a specific engineering field - in this case SHM. New population-based methods are proposed. For populations of near-identical structures, the idea of the 'form' of a structure is presented. The form is created to represent all individuals in a population, if damage data are available for an individual turbine in a wind farm, they can be transferred into the form and thus allow inference across the farm. Furthermore, a general theory of populations of disparate structures will be constructed using ideas from mathematics and computation: geometry, graph theory, complex networks and machine learning. Again, the theory will allow damage data from individuals to generate insights across the population.