Decision cycles across DoD industries are becoming increasingly disrupted by proliferation of data, new data sources and compute speeds within an increasingly volatile business environment. The digital twin is the key to effective decision-making in this new world.
Making better and faster decisions that can be executed perfectly every time is vital for delivering superior and sustainable business value. However, this is easier said than done because every individual perspective is under-pinned by a series of unique cognitive biases that drive swift action in adversity but make accurately weighing evidence, assessing probabilities, and deciding logically a challenge. Look no further than the constant discrepancy between strategic planning/ambition and results realization
A single view to the truth and analytics is therefore key to situational awareness and effective organizational decision-making. But are many players in the industry stuck on what type of analytics they need? The solution to this question should be driven by the problem that needs solving, not how much analytics can be thrown at data in the hope it will both find the problem, as well as solve it. The desired outcome should influence the type of analytics being sought and the available analytics technology that is fit-for-purpose.
Basic analytics technology can move data around and get Key Performance Indicators (KPI’s) displayed to the right people at the right time to enable decision-making, and this works well in hindsight for understanding what happened. However, increasing process plant complexity requires more sophisticated ways of approaching KPIs and targets.
In some cases, a rudimentary approach to KPI’s setting and monitoring can even become ineffective and counterproductive. In this case, deeper analytics technology, utilizing a digital twin, is necessary to account for the multi-dimensional factors and non-linear trade-offs that make effective decision-making a challenge.
The digital twin allows “What if?” and “What’s best?” scenarios to be run automatically to determine available strategies that maximize profitability. Experts can then review the recommended strategies to assess the impact of each recommended approach.
A digital twin works in the present, mirroring the actual device, system or process in simulated mode, but with full knowledge of its historical performance and accurate understanding of its future potential.
Therefore, the digital twin can exist at any level within some traditional architectures and can be defined as a decision support tool that enables improved safety, reliability and profitability in design or operations. It is a virtual/digital copy of a device, system or process that accurately mimics actual performance, in real-time, that is executable and can be manipulated, allowing a better future to be developed.
A digital twin is useful across the entire lifecycle of an asset. It is ideally created during the initial study to evaluate the feasibility and process model of the asset. It is then used and further developed during the design, construction and commissioning of the asset; thereby facilitating the optimum design of the asset and the training of the staff who will operate the asset. During the bulk of a plant’s lifecycle, operation and maintenance activities, the digital twin can be employed for optimization and predictive maintenance.
The digital twin provides the opportunity to see inside assets and processes and perceive things that are not being directly measured. It is wired up so that insights are instantly available without data and model wrangling by end users and run in a consistent way that can be understood and agreed upon. In this way the digital twin is able to drive agility and convergence in understanding and implementation across the whole business, for example from Engineering to Operations; Operations to Supply Chain; Reservoir to Facilities; Shop floor to Board room; etc.
- The Digital Twin merges physics-based system modeling and distributed real-time process data to generate an authoritative digital design of the system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Several key enabling techniques as well as a case study in a production line are addressed to validate the proposed approach. Several new advanced manufacturing strategies are issued to achieve smart manufacturing, resulting in the increasing number of newly designed production lines. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of production lines are essential to bridge the gap between design and operation.
- Factory design offers many promising capabilities regarding productivity and floor utilization. To evaluate the design and help the designer to escape design flaws, digital twin is proposed to support factory design. With considering of frequently changing in design phase, we present a modular approach to help building flexible digital twin and conducting corresponding changes. By using flexible digital twin, designer can quickly evaluate different designs and find design flaws in an easy way. And consequently time saving can be benefited. A case study of application on real factory is presented to illustrate the advantage.
- Digital twin provides an effective way for the digital-physical integration of manufacturing. Smart manufacturing services could optimize the entire business processes and operation procedure of manufacturing, to achieve a new higher level of productivity. The combination of smart manufacturing services and digital twin would radically change product design, manufacturing, usage, maintenance and other processes. Combined with the services, the digital twin will generate more reasonable manufacturing planning and precise production control to help achieve smart manufacturing, through the two-way connectivity between the virtual and physical worlds of manufacturing. Here we specify and highlight how manufacturing services and digital twin are converged together and the various components of digital twin are used by manufacturers in the form of services.
- Multiple forms of digital transformation are imminent. Digital Twins represent one concept, where we may use tools and technologies to “map” data from objects. It is gaining momentum because the “map” can act as a “compass” to reveal the status of (things, devices, components, machines, people, process visibility and real-time transparency. Adoption of digital proxies, or digital duplicates, may face hurdles due to lack of semantic interoperability between architecture and standards. The technologies necessary for automated discovery are in short supply. Progress depends on the convergence of information technology, operational technology and protocol-agnostic telecommunications. Making sense of the data, ability to curate data, and perform data analytics, at the edge. Delivering algorithm engines to the edge, are crucial for edge analytics, if latency is detrimental. The confluence of these, and other factors, may chart the future path for Digital Twins. The number of unknown unknowns, and the known unknowns, in this process, makes it imperative to create global infrastructures and organize groups, to pursue the development of fundamental building blocks. We need new ideas and research, to generate creative and innovative solutions.
- Digital twin implements concepts that try to solve the problem of handling large amounts of data that is accessed concurrently and has numerous internal semantic dependencies. Here we provide an application of digital twin through the adoption of simulation techniques. The sample takes inspiration from a real plant, which produces anchoring plates for electric motor brake discs The weakness point of the cycle is represented by problems arising from its discontinuity. To simulate and study solutions, software was used to create virtual simulation models. Here we optimize a machining cycle for electric motors brake disk support plates. Today it is possible to adopt modeling and simulation techniques that thanks to the increasing power of technologies and the Industry network platform allow understanding the impacts of changes in the production and therefore verifying its effectiveness and limits.
- The vision of the Digital Twin itself refers to a comprehensive physical and functional description of a component, product or system together with all available operational data. This includes more or less all information which could be useful in all - the current and subsequent - lifecycle phases. One of the main benefits of the Digital Twin for mechatronic and digital-physical systems is to provide the information created during design and engineering also at the operation of the system. The comprehensive networking of all information, shared between partners and connecting design, production and usage, forms the presented paradigm of next generation Digital Twin. This will bridge the gap between physics-based design simulation and its use in operation and service phases. Based on the example of a point machine the benefits of using the Digital Twin are shown.
- Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial object networks datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. Here we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.
- For fast integration of new vehicles, a current digital image of the real production plant ─ the Digital Twin ─ is groundbreaking. This Digital Twin of a factory consists of a current bill of resources for cost calculation and a current layout planning state. Here we describe a concept for creating a Digital Twin of a body production system for the concept and rough planning projects. In the internal concept planning phase, planners do cost calculations and layouts for ordering factory suppliers. However, for integration planning, the original concept and rough planning project have to be updated. Therefore, a new concept has been developed which uses current information from the digital-physical system and a current 3D scan to update the bill of resources and the layout planning on demand. Increasing competition in the automotive industry makes cost-saving integration of more and more vehicle derivatives and variants as well as electrical and combustion engine models into existing production systems. In contrast to the original concept and rough planning state, the automated production plants are continuously optimized during the detail planning phase as well as after the start of production as a result of improved processes and model upgrading
- The concept of Industry network of objects and smart factories has increasingly gained more importance. One of the central aspects of this innovation is the coupling of physical systems with a corresponding virtual representation, the Digital Twin. This technology enables new powerful applications, such as real-time production optimization or advanced network services. To ensure the real-virtual equivalence it is necessary to implement multimodal data acquisition frameworks for each production system using their sensing capabilities, as well as appropriate communication and control architectures. Here we extend the concept of the digital twin of a production system adding a virtual representation of its operational environment. We describe a proof of concept using an industrial robot, where the objects inside its working volume are captured by an optical tracking system. Detected objects are added to the digital twin model of the cell along with the robot, having in this way a synchronized virtual representation of the complete system that is updated in real time. The paper describes this tracking system as well as the integration of the digital twin in a networked 3D virtual environment that can be accessed from any compatible devices.
- With the recent advances in industrial object networks, the significance of information technologies to modern industry is upgraded from purely providing surveillance-centric functions to building a comprehensive information framework of the industrial processes. Innovative techniques and concepts emerge under such circumstances, e.g. Digital Twin, which essentially involves data acquisition, human-machine-product interconnection, knowledge discovery and generation, and intelligent control, etc. Signal processing techniques are crucial to the above-mentioned procedures, but face unprecedented challenges when they are applied in the complex industrial environments. Here we survey the promising industrial applications of object network technologies and discuss the challenges and recent advances in this area. We also share our early experience with a real-world industrial system that enables comprehensive surveillance and remote diagnosis for ultra-high-voltage converter stations. Challenges in building such a system are also categorized and discussed to highlight potential future directions
Top 10 Digital Twin Objectives Building Effective Communication between Physical/Information Domain with Performance Maps
Digital Twins provide accurate representations of a device, system or process over its full range of operation and its full lifecycle. Ideally the digital twin should be able to transition from design to operations with ease.
Data quality issues be identified and mitigated so the digital twin can be trusted to reflect reality and relied on for quality and accuracy of its predictions. While individual point solution digital twins exist today, future success stories may involve multi-purpose digital twin. It is unrealistic to achieve a future state in one step, but more likely to be achieved by the connectivity of valuable high performing individual elements. Must be agile – think big, start small, scale fast and drive adoption.
- Digital Twin application framework includes changes in conceptual design matching/validation stages with model approach saves workload/time for manufacturing process and production system
- Digital Twin allows designers to forecast product behavior and reduce inconsistencies through verification of virtual products without waiting for prototype shortens design cycles
- Digital Twin provides effective method to draw up plan and optimize process in production execution by pre-designed modules so only series of parameters need to be modified to conduct simulation under different design scenarios
- Digital Twin builds shop-floor with specialized work models include physical space, rules, behavior, dynamics so workload and development time can be saved
- Digital Twin links collection of digital artifacts include engineering data, operation data and behaviour descriptions via multiple simulation models
- Digital Twin simulation models are specific for intended use and apply suitable fidelity for problem solutions, growing along with the real system for whole life cycle and integrates currently available data
- Digital Twin allows performance/utilization data created during design and engineering to be available/ready for evaluation during operation of system
- Digital Twin transports data, information and executable models of all system elements from development, products delivery by supplier to operation.
- Digital Twin embedded/available on edge device creates loop between real world and digital world as prerequisite for autonomous systems
- Digital Twin controls service system access models identify/control failure behavior to invoke repair, or replace the broken-down equipment
Top 10 Digital Twin Technology Benefit Multiple Levels of Organization Investment Plan Align Production with Supply Chain Processes
Manufacturers have successfully achieved development of an integrated production management system digital twin that operates across the entirety of the process manufacturing supply chain and asset lifecycle to align production management and reliability, supply chain optimization, and strategic asset investment planning.
- Enterprise Insight: A series of business and financial Key Performance Indicators (KPI’s) are updated in real-time, plan versus actual, as part of an enterprise-wide balanced scorecard for corporate situational awareness. Advanced logic is applied to real-time operations data to project current and future understanding of the business for executives.
- Capability Assurance: Key operator actions are captured, controlled and manipulated in real-time through monitoring and control of work processes. Minimize the learning curve for new operators, support change management and enable vastly improved scenario validation through operator training simulation and modular procedure automation solution.
- Advanced Production: Multivariable predictive controls drive the plant continuously to its optimum constraints by reacting to disturbances in a closed loop.
- Value Chain Optimization: Drive agile and efficient alignment of supply of premium products as closely as possible to market demand with sufficient resilience or operational flexibility to readily adjust production. Exploit market opportunities through supply chain planning, scheduling and production accounting.
- Automation and Control Integrity: Digital representation of the live plant and its automation algorithms through the “twin” function of an Integrated Control and Safety System allows engineers to conduct fundamental process control tests at an engineering workstation, as well as any proposed adjustments, before they are applied on the live plant
- Instrumentation and Equipment Productivity: Minimize the need for breakdown and preventative maintenance through advanced online monitoring and prediction of field device health, and process interface conditions that reduce unnecessary trips
- Advanced Chemistry: Highly intelligent devices, such as pumps, flowmeters, transmitters, and chemical analyzers provide total insight into their ongoing performance as well as the ability to adapt to changing duty requirements throughout the measurement device lifecycle.
- Plant Processes Optimization: Online and offline high-fidelity models for non-linear performance monitoring, simulation, and optimization using first principle kinetics deliver optimized yield performance, flow assurance, energy efficiency improvement, enhanced reliability and operator capability assurance.
- Strategy execution Ensuring field and line employees have the information they need to understand the bottom-line impact of their day-to-day choices
- Facilitating information flows across organizational boundaries to minimize second-guessing of decisions
Top 10 Process Automation System Factory Acceptance Test Simulated from Simplified Model Signals Allows Operator Training/Case Studies
The control system simulator used with the dynamic simulator must run the exact application software without deletions or modifications from the plant system. In order to provide a solution that can cost effectively be kept current with the plant system, it is essential that the control system simulator reflect the control system configuration with no additions or deletions.
- Strategy for the Digital Twin is defined and the functional requirements are developed using simulation to review process design/control plan and identify control and operational issues early in plant design.
- Automation System Design / Implement / Test - during control system engineering, the dynamic simulation is integrated to the control system simulator. This begins the use of the integrated Digital Twin. The Digital Twin is used to test control system configuration and graphics. Control design can be evaluated early in the project when rework and changes will have the least project impact.
- Factory Acceptance Testing - including system integrity and operational tests are done with the Digital Twin. Operating procedures are tested and refined. Problems and issues that could delay unit startup or disrupt production are caught before they impact the project. Loops are initially tuned to support smooth startups.
- Operator Training – begins early, at the completion or concurrent with factory acceptance test. Operators begin developing competency well before the startup of the plant or the commissioning of the process automation system using the Digital Twin.
- Structured training and open exploration of process dynamics and control system performance are both valuable in preparing the operator for actual plant operations.
- Process Control Improvements are developed, tested, and demonstrated to operations management without affecting the operation or production of the actual plant. The control system configuration developed in the Digital Twin is exported directly to the process automation system minimizing operational risk.
- Training New Operators on process operations, startup and shutdown procedures, and hazardous or infrequent process occurrences, is accomplished without affecting the running process.
- Evaluating New and Experienced Plant Operations is done on pre-developed training scenarios. Plant operations competency requirements are established and reinforced with repeatable, measurable, documented training sessions.
- Process Optimization Studies are done on the Digital Twin providing the process engineers with a tool that accurately models the process dynamics not seen in steady-state design models. Process changes with control improvements are thoroughly tested before construction begins, reducing rework and startup times.
- Process Safety Design is tested on the Digital Twin without impacting the process. Levels of Protection in Operating procedures are verified before implementation. Capital investment decisions are validated and optimized using the Digital Twin.
Top 10 Digital Initiatives within Reach for Manufacturing Industries Plant Connectivity Accelerate Decision Cycle Creates Sustainable Business Value
Digital twin must exist within a strong oversight framework. This includes challenges to create well defined business processes along with clarity around the decision rights/actions responsibility of workers Must considerer guidance from subject matter experts and their associated analytical insights working remotely from the operational location. Currently these insights are obtained only when there is a problem or there are structured inputs on a regular basis, such as periodic service review schedule.
- Reduce field qualification requirements and minimize field deviations
- Speed up isolation of issues to individual objects for Change Management
- Enforce consistent testing of the automation by using automated testing scripts to accelerate and reduce manual testing
- Catch configuration and code regression errors early in the project cycle when remediation is best cost and operation costs are minimized
- Perform a subset of software commissioning/shakedown activities prior to installing the code on the production equipment. This can reduce the time needed to perform testing on the production equipment, reducing the startup time for the plant
- Dynamic process models must be first-principles, following fluid dynamics, kinetics, and thermodynamics laws, but must also be dynamic and real-time. Real-time response must support good process dynamics for even fast processes without changes to process control system loop tuning.
- Provide cost-effective, consistent approach, that maximizes the return on investment in the model development using A multi-purpose approach
- Allow incremental model development, enhancement, and tuning to support the evolving requirements of the lifecycle.
- Ability to start with a model with preliminary data and then to tune it with detailed engineering or actual data
- Simulation must support a wide range of process model complexity from simple inputs/outputs pushbuttons, switches, and sensors models to complex unit operations models.
Top 10 Reasons to Exploit Cloud for Hosting Digital Twin Gaining Interest in Industries as Key Enabler of Digital Enterprise
The Cloud is already the infrastructure of choice for most business applications. However, it remains unexploited for most operational applications. The reason is that most valuable operational applications rely on a continuous feed of plant data, which means they can never be isolated from the plant in a way that a worker performance management system or capital budgeting system can.form a network point of view, the Cloud offers some compelling savings vs on-premise. But unless the operational risk associated with exposing the plant to the Cloud is offset by value created by the people and applications it serves, its use will remain marginal.
- Can engage people and technologies from outside corporate boundaries
- Build Augmentation with visualization models in resource-constrained environments
- Allows remote subject matter experts to join in the day-to-day troubleshooting
- Contribute to profit improvement activities of the plant
- Enables the digital twin to subscribe to external data feeds that can enrich its resolution.
- Allows analytical capabilities to be offered remotely by experts.
- Supports and nourishes agility with respect to the digital twin.
- Allows experimentation and rapid deployment of new solutions.
- Makes solution updates trivial and significantly reduces infrastructure costs.
- Reduces the cost of termination – if a solution does not work out as expected, a cloud solution can often be switched off with little to no on-going cost.