decision to take action and that action fails, or likewise, if failure results from deciding to take no action, against the recommendation of the digital twin. Costs are also an issue, particularly in an industry that tends to remain comfortable with reactive maintenance models and condition monitoring, and there are no generally accepted, accurate ways of calculating what kind of savings such systems could offer, if it could alter and inform the ‘way we have always done things.’ Indeed, it has taken much learning to make these digital tools, and it will doubtless take much learning before we can use them effectively in driving efficiencies into our practices as well. Currently, digital twin technologies find their use cases across a wide variety of business and industry applications1: >> Manufacturing: Substantial influence on maintenance and product design. >> Industrial IoT: Monitoring, tracking, and controlling of systems. >> Healthcare: Reducing expense and providing tailored support to patients. >> Smart cities: Planning, building, administration of resources, and decreasing environmental impact. >> Automotive: Performance efficiencies, behavioural and functional modelling. >> Retail: Customer experience enhancement, inventory control, and modelling of consumer behaviour. Indeed, just based on the above list, we can, and should, imagine a tremendous number of use cases for digital twinning in offshore wind, both for fixed and floating (FLOW) facilities. Engaging digital twinning is important to offshore wind because of one major aspect that makes offshore wind unique compared to that list: offshore wind operations occur in remote, harsh, risk-laden marine environments. Anything that reduces risk in this type of environment by cutting the need for frequent transit is worth exploring, especially considering the long-term commitments that have been made in offshore wind – normally 25 years for a fixed-foundation farm in the North Sea.
Figure 1. Knowledge management sophistication.
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By combining known offshore wind industry pain points with conceptual use cases for digital twins, we can begin to see a number of solutions forming on the horizon, some of which are achievable today with modest cost and effort. Also, if we accept that there are only two fundamental ways of creating more value in offshore wind – making more electricity or spending less money – and it is accepted that the maximum generation capacity is capped by the physical limitations of the farm itself and the conditions in which it operates, cost-savings and efficiencies as primary drivers for the industry should be turned to, specifically for maintaining asset availability. With regard to addressing pain points, this article will briefly examine digital twinning for use in data aggregation, condition monitoring, and driving efficiencies into maintenance regimes using optimisation techniques, as they relate to cost-savings. It is worth mentioning that the planned zero-subsidy transition over the next four years also helps add a sense of urgency to the mix.
Data aggregation and knowledge management In interacting with numerous entities across the offshore wind space on a range of different levels and topics, the one overriding pain point that is repeated in many conversations is ‘just getting all of our information together in one place’ – i.e. data aggregation, then data management going forward once all that data has been aggregated and collated. In offshore wind, this pain point is most acute between phase handovers (i.e. design to construction, construction to commissioning, commissioning to O&M, etc.) and between entity handovers (i.e. designer to constructor to commissioner, between owners, managers, and contractors). The commonality here is simply that any time data or information gets transferred, some portion of it gets lost in the process. In the field of information systems, prevention of this phenomenon is known as knowledge management (KM), because it deals not just with the transfer of raw data, e.g. from sensors, but also all sorts of other documentation such as design drawings, engineering specifications, inspection reports, commissioning certificates, photogrammetry, survey data, etc. The list goes on if the goal is to create a true digital twin for an offshore asset. KM also goes beyond document control, which helps form the structures and substructures of KM by applying consistent nomenclatures, organising premises, version and permissions controls, and so forth. Likewise, KM must also go beyond simple condition monitoring and reactive alarming in terms of a fourth-dimensional element that tracks condition trends over time, eventually feeding predictive
