6 August 2020
All models are wrong, but some are useful
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.
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:
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.
To provide benefits across an entire enterprise, a digital twin must be more than a single entity. Fully-featured digital twins will be realised as a family of twins, each of which matures and grows in depth and value through its life.
The models used to support asset performance may differ from those used in design and development, but there will be clear data linkages across all of these model sets that need to be managed. Twins will both consume and create high volumes of data throughout their lives.
Several coherent twins will be created in the design phase, from basic 3D geometric and design information through to complex high order simulation models and wider design information management. This "Design Twin" set will mature through the build phase to create a "Manufacturing Twin" - a key enabler for the future digital shipyard.
This evolution will continue through to the in-service phase, where sensor inputs from the physical asset will help realise the final "Digital Twin." Further models will need to be created at this point, typically fast surrogate models that include a measure of their own uncertainty to effectively provide predictive and prognostic outputs in response to real-time input data.
The creation of a twin is therefore a complex process and relies upon a continuous digital thread throughout the lifecycle.
Although IoT is a key enabling technology for digital twins, twins are much more than a simple IoT implementation, which may integrate an intelligent sensor with a data platform.
Twins bring together on-platform sensing with other data sources (e.g. environmental data, historical maintenance data) and match this data with a set of models, applying analytics and machine learning to enable predictive and prognostic insights. These insights provide effective decision support in an intelligent asset management context.
Due to the complexity and volume of data, the effective visualisation of twin outputs is critical to enable usability and layered consumption of data.
The concept of a digital twin isn’t a new one - the term was first used in 2002 and the original information concepts date back to the 1980s. However, there are several enabling factors that are coming together to accelerate the implementation of twins.
The first of these is cheap, high bandwidth sensing availability - IOT is a key enabler for twins. The second is ‘big data’, there is now an abundance of cheap data storage and associated advances in data management and mining.
The third is the increased accessibility of the high-performance computing (HPC) required to manage the computing burden of a functioning twin.
Finally, advances in integrated design and analysis environments are creating key digital threads at the start of the lifecycle, enabling the later creation of a twin.
These design digital threads link 3D information and multi-physics with integrated data management to create a configuration managed environment that can be maintained and augmented through production.
Within the defence maritime enterprise, many of the challenges to adoption do not just come from the technical domain, but also as products of the commercial and supply chain environment.
Defence maritime has traditionally contracted separately for design, build and maintenance contracts. These contracts are focused on providing physical equipment rather than data and models and this approach typically requires the involvement of multiple prime contractors for each asset through life, along with a correspondingly complex supply chain network.
There is currently no mechanism to allow data and digital models to flow freely and securely through this enterprise whilst protecting Original Equipment Manufacturer’s intellectual property. It is likely that a future digital twin for defence maritime will span multiple solution platforms, multiple companies, and multiple solution platform vendors.
BMT sees digital twins as a key element of industry 4.0, having seen the benefits and savings they are bringing to our customers. BMT’s Whole-of-Mine Digital Twin is just one of many bespoke examples.
Built on our Remote Operations Automation and Robotics (ROAR) software platform and using lidar mapping capabilities and expertise in 3D algorithmic decision making, our solution integrates precise terrain data, machine data, health data and production metrics into a single 3D immersive and intuitive interface.
This allows production teams and planners to measure, monitor, report and optimise mining operations in real-time, reducing costs and risks.
BMT continues to develop and expand our digital twin technology into other submarkets, with ongoing projects that are delivering Autonomous TLO (Train Load Out) Digital Twins, Hopper Surface Mapping and Wagon Surface Mapping solutions for train unloading rail operations, for example.
Other technologies are in development for Stockyard Mapping and Ship Hold Surface Mapping for bulk materials handling customers in ports.
To support future maritime defence digital twins, we’re taking a multi-threaded approach through a range of internal strategic initiatives and R&D activities, as follows:
These solutions not only highlight our abilities and expertise in developing bespoke customer solutions, but are also are testament to our commitment to Digital Transformation programmes in the industries we serve.
Research and Development Lead, BMT
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.
To contact Jake, please email email@example.com.
Head of Systems Engineering, BMT
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.
To contact Ross, please email firstname.lastname@example.org.