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

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.

A major issue in industrial take-up of structural health monitoring (SHM) technology is the complexity of the problem for individual structures. The population-based SHM concept can provide a paradigm shift by moving to a framework where inter-structure information and data are leveraged to massively augment the results of individual structure SHM. SHM ultimately allows a transition to condition-based maintenance, leading to lighter, greener, safer structures with substantially lower cost of ownership and operation.

The wind energy sector is targeted in this extension; wind energy needs to play a vital role in meeting UK targets for energy from renewables. A major reason for the shortfall in expectations for UK round 1 wind farms was low availability. The UK currently produces more electricity from offshore wind than any other country in the world; wind energy generates around 5% of annual UK electricity needs and this is expected to double to 10% by 2020. It is clear that technologies, like those proposed here, that can ultimately lower the Levelised Cost of Electricity are of significant national importance. The industrial partners in the project will benefit significantly; Siemens are the largest producers of offshore wind turbines in the world, technologies reducing downtime of turbines will be of inestimable value to them. This is also clearly true for offshore wind farm operators like Vattenfall.

The principal means of ensuring industrial impact is to ensure that the proposal is industry goal-driven. To facilitate this, the Steering Group (SG) from the original proposal will continue, augmented by new members from the new partners in the offshore wind industry - Siemens and Vattenfall. New population-based ideas will prove powerful in other contexts; continued discussion with the Advanced Manufacturing Research Centre in Sheffield will lead to manufacturing applications; discussions with the NetworkRail Innovation & Technology Centre at Sheffield will focus on railways. The workshop programme of the original proposal will be extended through the provision of two new workshops concentrating on the new form-based/network-based technologies proposed here.

SHM software at the Engineering Institute in LANL will be enhanced by embedding the new ideas of population-based SHM; as the LANL software is extensively downloaded, this is an effective dissemination channel. The LANL ECHO software will be significantly extended by converting it from a database to an ontology. By the PI and PDRA lecturing at the LANL Summer School, their students will be exposed to population-based concepts. As the students go on to employment with major US companies or PhD projects in the most prestigious universities, there is no better way to propagate the new ideas there.

The new ideas here and their applications to renewable energy are likely to generate public interest. Presentations at Cafe Scientifique and on regional radio have been successful in the past and will be used again. Outreach into professional engineering will be via seminars in the programmes of professional institutions. Schoolchildren will see the research via demonstrations and interactive activities for the University of Sheffield Engineering Summer School. The work of this proposal will also generate case study material for the new UoS Pathways to Engineering scheme, which is an initiative designed to attract students not traditionally drawn to engineering, and therefore lacking physics qualifications. This is part of UoS strategy for widening participation and, in particular, making an engineering career more accessible to girls. A web site will be maintained with public and private pages; some pages being tailored to communication to the lay public and media.