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WE PAVE THE WAY FOR DATA-BASED ECOSYSTEMS – PLATTFORM INDUSTRIE 4.0
The digital twin is a comprehensive representation of a product or process into a digital world; it plays a vital role in the product lifecycle and helps to the horizontal integration of the production process. Digital Twin is one of the most promising technologies for system simulation and monitoring. Nevertheless, the concept is relatively new and has different conceptions and applications in the literature. In this work, a review of the State of the Art on principal scientific databases is presented. Besides, a brief classification regarding the main applications found are provided and a proof of concept based on a vehicle chassis.
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.
With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implemen-tation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway, United Kingdom (UK), Canada, China, Italy, Netherland, Brazil, Germany, and Saudi Arabia. The overall publication rate was less than ten articles (approximately) per year until 2017, and a significant increase occurred in 2018 and 2019. The number of journal publications was noticeably lower than the number of conference publications, and the majority of the publications presented theoretical concepts rather than the industrial implementations. Both these observations suggest that the DT implementation in the O&G industry is still at an early stage.
Understood as an industry concept recently proposed that encompasses the main technological innovations of the fields of automation, control and information technology applied to the manufacturing processes, Industry 4.0 is a real challenge for today’s businesses. Indeed, from cyber-physical systems, Internet of Things and Internet Services, the pro-duction processes tend to become increasingly efficient, autonomous and customizable. The relationship with costumers is also more effective. In other words, the availability of real-time data enables organizations to offer more personalized products and customized solutions, which usu-ally originates significantly higher margins than mass-manufacturing offers. Taking in mind these changes and implications in organizations, the present book, entitled Industry 4.0 – Challenges, Trends, and Solutions in Management and Engineering, looks to provide a support to academ-ics, researchers and practitioners, as well as all to those who operate in the Management and Engineering fields and need to deal with the rapid pace of digitization in industry today. Becoming more active in Industry 4.0, these organizations see their benefits go far beyond extending the digital reach or selling new types of products and services. Industry 4.0 will establish these organizations, their employees as well as their total ecosystem of stakeholders, namely, suppliers, partners, distributors and customers as a complete interconnected and integrated digital network, linked to other networks around the world.
Germany-wide test scenarios for the efficient way in industry 4.0 innovations
June 2017
Labs Network Industrie 4.0
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