A digital twin framework for predictive maintenance in industry 4.0

The rapid advancements in manufacturing technologies are transforming the current industrial landscape through Industry 4.0, which refers not only to the integration of information technology with industrial production, but also to the use of innovative technologies and novel data management approaches. The target is to enable the manufacturers and the entire supply chain to save time, boost productivity, reduce waste and costs, and respond flexibly and efficiently to consumers’ requirements. Industry 4.0 moves the digitization of manufacturing components and processes a step further by creating smart factories. Within this context, one of the key enabling technologies for Industry 4.0 is the adoption and integration of the Digital Twin (DT). However, most of the DT solutions provided by the current leading vendors are in fact digital models or digital shadows, and not digital twins. This is due to the fact that there is no common understanding of the definition of the DT amongst the leading vendors, and its usage is slightly different but showcased under the same umbrella of DT. In this paper, a DT framework is proposed that replicates the processes of a real production line for product assembly using the Festo Cyber Physical Factory for Industry 4.0 located at Middlesex University. Moreover, the paper introduces a viable framework for interlinking the physical system with its digital instance in order to offer extended predictive maintenance services and form a fully integrated digital twin solution

A digital shadow cloud-based application to enhance quality control in manufacturing

In Industry 4.0 era, rapid changes to the global landscape of manufacturing are transforming industrial plants in increasingly more complex digital systems. One of the most impactful innovations generated in this context is the "Digital Twin", a digital copy of a physical asset, which is used to perform simulations, health predictions and life cycle management through the use of a synchronized data flow in the manufacturing plant. In this paper, an innovative approach is proposed in order to contribute to the current collection of applications of Digital Twin in manufacturing: a Digital Shadow cloud-based application to enhance quality control in the manufacturing process. In particular, the proposal comprises a Digital Shadow updated on high performance computing cloud infrastructure in order to recompute the performance prediction adopting a variation of the computer-aided engineering model shaped like the actual manufactured part. Thus, this methodology could make possible the qualification of even not compliant parts, and so shift the focus from the compliance to tolerance requirements to the compliance to usage requirements. The process is demonstrated adopting two examples: the structural assessment of the geometry of a shaft and the one of a simplified turbine blade. Moreover, the paper presents a discussion about the implications of the use of such a technology in the manufacturing context in terms of real-time implementation in a manufacturing line and lifecycle management. Copyright (C) 2020 The Authors

Definition of the Future Skills Needs of Job Profiles in the Renewable Energy Sector

The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce.This research was partly funded by (a) the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B)*, (b) the 4gune cluster, Siemens Gamesa and Aalborg University through the project “Identification of the necessary skills and competences for professionals of the future renewable energy sector”, and (c) Lantek, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair

Digital Twin Fidelity Requirements Model for Manufacturing

The Digital Twin (DT), including its sub-categories Digital Model (DM) and Digital Shadow (DS), is a promising concept in the context of Smart Manufacturing and Industry 4.0. With ongoing maturation of its fundamental technologies like Simulation, Internet of Things (IoT), Cyber-Physical Systems (CPS), Artificial Intelligence (AI) and Big Data, DT has experienced a substantial increase in scholarly publications and industrial applications. According to academia, DT is considered as an ultra-realistic, high-fidelity virtual model of a physical entity, mirroring all of its properties most accurately. Furthermore, the DT is capable of altering this physical entity based on virtual modifications. Fidelity thereby refers to the number of parameters, their accuracy and level of abstraction. In practice, it is questionable whether the highest fidelity is required to achieve desired benefits. A literary analysis of 77 recent DT application articles reveals that there is currently no structured method supporting scholars and practitioners by elaborating appropriate fidelity levels. Hence, this article proposes the Digital Twin Fidelity Requirements Model (DT-FRM) as a possible solution. It has been developed by using concepts from Design Science Research methodology. Based on an initial problem definition, DT-FRM guides through problem breakdown, identifying problem centric dependent target variables (1), deriving (2) and prioritizing underlying independent variables (3), and defining the required fidelity level for each variable (4). This way, DT-FRM enables its users to efficiently solve their initial problem while minimizing DT implementation and recurring costs. It is shown that assessing the appropriate level of DT fidelity is crucial to realize benefits and reduce implementation complexity in manufacturing

Blockchain technology to secure data for digital twins throughout smart buildings’ life cycle in the context of the circular economy

Blockchain technology (BCT) can be leveraged for digital twins (DT) to enhance data security, collaboration, efficiency, and sustainability in the construction industry (CI) 4.0. This study aims to develop a novel technological framework and software architecture using BCT for DT throughout the lifecycle of smart building projects in the context of the circular economy (CE). The study identifies key challenges and technological factors affecting BCT adoption. It also identifies which project data types can benefit from BCT and the key factors and non-functional requirements (NFRs) necessary for the adoption of blockchain based digital twins (BCDT) in CI 4.0. The study finally proposes a software architecture and smart contract framework for BCDT decentralized applications (DApps) throughout the lifecycle of smart infrastructure projects. The study offers a technological framework – the decentralized digital twin cycle (DDTC) – with BCT to enhance trust, security, decentralization, efficiency, traceability, and transparency of information. The study found that the key data from the project lifecycle relevant for BCDTs relate to the BIM dimensions (3D, 4D, 5D, 6D, 7D, and 8D) and a novel contractual dimension (cD) is also proposed. Additionally, BCDT maturity Level 4 is proposed, leveraging BCT to enhance collaboration, process automation, and data sharing within a decentralized data value chain. The main NFRs for BCDTs are security, privacy, interoperability, data ownership, data integrity, and the decentralization and scalability of data storage. A five layered software architecture and a smart contracts framework using Non-Fungible Tokens (NFTs) are offered to address key industry use cases and their functional and non-functional requirements. The framework narrows the gaps identified around network governance, scalability, decentralization, interoperability, energy efficiency, computational requirements, and the integration of BCT with IoT, BIM, and DT. A cost analysis permitted developing criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 based on key blockchain properties (security, decentralization, scalability, and interoperability). The study provides an industry-specific analysis and technological approach for BCDT adoption to address key challenges and improve sustainability for the CI 4.0. The findings provide key building blocks for industry practitioners to adopt and develop BCDT DApps further. The framework enables a paradigm shift towards decentralized ecosystems of united BCDTs where trust, collaboration, data sharing, information security, efficiency, and sustainability are improved throughout the lifecycle of smart infrastructure projects within a decentralized CE (DCE)

Standardization Framework for Sustainability from Circular Economy 4.0

The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl