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Reference Number EP/R003645/1
Title Structural Health Monitoring of Systems of Systems: Populations, Networks and Communities
Status Completed
Energy Categories RENEWABLE ENERGY SOURCES(Wind Energy) 70%;
NOT ENERGY RELATED 30%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Professor K Worden
No email address given
Mechanical Engineering
University of Sheffield
Award Type Standard
Funding Source EPSRC
Start Date 01 July 2018
End Date 31 January 2022
Duration 43 months
Total Grant Value £881,028
Industrial Sectors Energy
Region Yorkshire & Humberside
Programme Energy : Energy, NC : Engineering
 
Investigators Principal Investigator Professor K Worden , Mechanical Engineering, University of Sheffield (100.000%)
  Industrial Collaborator Project Contact , Los Alamos National Laboratory, USA (0.000%)
Project Contact , Siemens AG, Germany (0.000%)
Project Contact , Vattenfall Wind Power Ltd (0.000%)
Web Site
Objectives
Abstract 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. Thefirst 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.
Publications (none)
Final Report (none)
Added to Database 18/02/19