Focus July 2020

15 A good deal of progress has been made across several sectors since the original inception of the twin concept in 2002, which initially began by gaining momentum within high-value product manufacturing industries, such as mining, automotive and aerospace. Typically, specific elements of a twin were developed to help achieve a specific aim for a specific system or sub-system. Since then, the digital twin concept has proliferated, taking on many interpretations and variations and has found increasing relevance across many other sectors. However, there’s still much to be done before the benefits of twins can be realised across industry on a wider scale. For instance, there are no standards in place for such tools, and many challenges yet remain around open data capture, data storage, and performing effective analysis to support decisions with confidence. Indeed, a standard definition of a digital twin has not yet been established and there are many variations in the accepted scope of a twin across sectors. To add further complexity, what began as a niche engineering model is increasingly being expanded in scope and aligned to wider digital transformation initiatives. The ‘digital twin’ is now a recognised core component of the Industry 4.0 journey, helping organisations understand their complex processes, resources and data to provide insight into their business and help optimise their operations. Demystifying Digital Twins All models are wrong, but some are useful But while ‘digital twins’ can sometimes mean different things to different people, we at BMT define them as ”precise, virtual copies of machines or systems”. These digital models receive regular data input from the relevant physical system, thus providing intelligent support for operational decisions. Twins are, first and foremost, learning systems, driven by data that’s collected from sensors in real time. This means that sets of complex digital models can adapt to mirror every element of a product, process or service. The efficiencies of an asset digital twins’ product life-cycle management can typically offer powerful benefits in four areas: 1. Operations optimisation Using variables like weather, fleet size, energy costs or performance factors, models are triggered to run hundreds or thousands of “what-if” simulations to evaluate readiness or make necessary adjustments to current system set-points. This enables system operations to be optimised or controlled during operation to mitigate risk, reduce cost or gain any number of system efficiencies. 2. Predictive maintenance In industry 4.0 applications, models can determine the remaining useful life of an item of equipment and advise operators on the best time to service or replace it. 3. Anomaly detection The model runs in parallel to the relevant real asset and immediately flags any operational behaviour that deviates from expected (simulated) behaviour. For example, a petroleum company may stream sensor data from offshore oil rigs that operate continuously and the digital twin model will look for anomalies in the operational behaviour to help avoid catastrophic damage. 4. Fault isolation Anomalies can trigger a battery of simulations to isolate the fault and identify its root cause, so that engineers, or the system, can take appropriate action. Research and Development Lead, BMT Jake is a chartered engineer and Honorary Professor at the University of Exeter. His role involves the portfolio management of BMT’s internally funded research work supporting our customers and strategic initiatives in a range of areas including Digital Transformation and AI. He also looks after academic engagement, ensuring new and emerging technologies from academia are pulled through into industry. He is part of BMT’s horizon scanning team, highlighting and mapping external signals and trends. These signals can be explored to stimulate thinking about the range of future possibilities. jake.rigby @bmtglobal.com Head of Systems Engineering, BMT Ross is currently the Head of Systems Engineering for BMT, specialising in Defence & Security. The team work across maritime, land, joint, and defence digital, providing full project lifecycle consultancy support to our customers. Ross has significant experience in integrated digital design and system simulation and modelling, and has worked across civil aerospace, defence, energy and medical sectors. He has implemented pioneering digital twin systems in the oil and gas and energy sectors and worked closely with academia on developing prognostic methods for condition based maintenance applications. Ross has a background in simulation and modelling of platform dynamics and platform systems, using these models to inform the design process and develop control and automation strategies and software. He has a strong interest in the use of digital twins at all stages of the project lifecycle to manage risk effectively and to optimise assets throughout their lifecycle. Ross Mansfield [email protected] 14 Jake Rigby Cont’d