With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is benecial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Digital Twin: Values, Challenges and Enablers

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

With the rapid pace of technological growth, it’s not always easy to imagine where digital

transformation is taking the manufacturing sector, but one good way of doing this is to take

a closer look at the “Digital Twin” concept within the industrial Internet of Things (IoT).

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As IoT connectivity provides the manufacturing sector with an increasing number of ways to

access sensor-driven data locked in industrial machines and equipment, the need for data

analysis, management, and control methods has also become more crucial.

The amount of data collected from monitoring a smart factory is enormous, but if that data

isn’t aggregated and organized in a way that can support the decision-making process, then

it’s of no use.

One method that’s proving to be invaluable to engineering and customer service teams that

are looking to leverage collected data is that of the “Digital Twin”.

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One of the most intriguing areas of technology-enabled potential for procurement and

supply chain operations is the “digital twin.” A digital twin is a digital representation, or a

virtual model, of a real system that enables businesses to understand the architecture and

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operation to predict operational performance. It helps enterprises reduce operating costs

and extend the life of machines and other physical assets, while acting as a predictive

“test bed.”

Digital twins serve as evidence that increased computing power and data access can

accelerate the speed companies can achieve without sacrificing effectiveness — in this

case through improved decision support. Being able to create a complete analog of a

real-world situation in the digital domain makes it possible to simulate the total effect of

multiple options in parallel. Once the optimal route is identified, the chosen direction can

be immediately carried out.

It is absolutely critical that the digital twin is fed in real time, and that the input data is

factual rather than being interpreted or assumed. By converging information from the

Internet of Things (IoT), data lakes and other external feeds, companies can reduce the

“leap” between the model and reality. The applicability of the recommendations derived

from working with a digital twin are therefore significantly more reliable than those made

possible by traditional, more artificial modeling.

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Emerging technologies such as IoT, AI and advanced modeling techniques are enabling an intelligent, connected and digitally empowered mesh of people, things and services which outline a definitive concept, Digital Twin, for today’s business. Digital Twin has moved beyond manufacturing sector, its traditional ecosystem, into all sorts of service and goods-based businesses ranging from automotive to healthcare. And, it is so imperative to business today, it has continuously named within Gartner’s top 10 Strategic Technology Trends for the past couple of years. In this white paper, we remove some myths spread about Digital Twin and answer your biggest questions: What is it? Why does it matter? What are the benefits and the challenges? Which are the building blocks? Upon reading this white paper, you will have a more clear and transparent understanding of Digital Twin and its applications, and will be able to better define

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your strategy for Digital Twin.

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• A timely monitoring and diagnosis scheme is proposed based on virtual systems.

• Three types of process faults are studied, and corresponding optimized control configurations are proposed.

• Both reconfiguration problems of bias faults and apparatus failures are considered.

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• Adaptive algorithms are integrated to design digital twins at both on-line and off-line stage.

• Proposed digital twins are appropriate for the dynamic systems with unknown model parameters.

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Fraunhofer Institute for factory

Operat ion and Automation IFF, Magdeburg

The Digital Twin: Guaranteeing Your Success

ARC VIEW

MARCH 3, 2016

One of the single most significant

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issues associated with projects is

the lack of accurate, complete,

and timely information that can

add substantial risk to the project

in terms of cost, time, and safety.

The Next Generation of ALIM: Connecting the

Digital Asset

By Dick Slansky and Paula Hollywood

Keywords

ALIM, CAPEX, OPEX, TOTEX, Digital Asset, Asset Reliability, Asset

Performance, Predictive Analytics, Internet of Things, Smart Connected

Assets, Cloud Infrastructure, Engineering Information, System of Record

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