New Approaches to Implementing the SmartJacket into Industry 4.0 ‡

Marcoň, P.; Arm, Jakub; Benešl, Tomáš; Zezulka, F.; Diedrich, Christian; Schröder, Tizian; Belyaev, A. D.; Dohnal, P.; Kriz, T.; Bradáč, Zdeněk

doi:10.3390/s19071592

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…The integration of cyber and physical parts plays an important role in state-of-the-art manufacturing systems [ 75 ]. Examples of some practical applications of the 4.0 industry are very diverse, and include steel [ 76 ] and mining [ 77 ] companies, smart jacket [ 78 ] and sunroof ambient light [ 79 ] production systems and construction industry applications [ 80 ]. The proposed formal verification of a CPS control module using an abstract rule-based logical model fits, then, perfectly into the contemporary trends and may be used in modern manufacturing automation systems.…”

Section: Discussionmentioning

confidence: 99%

Formal Verification of Control Modules in Cyber-Physical Systems

Grobelna

2020

Sensors

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The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used both for formal verification using the model checking technique and for prototype implementation in FPGA devices. The model is automatically transformed into a verifiable model in nuXmv format and into synthesizable code in VHDL language, which ensures that the resulting models are consistent with each other. It also allows the early detection of any errors related to the specification. A case study of a manufacturing automation system is presented to illustrate the approach.

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Section: Discussionmentioning

confidence: 99%

Formal Verification of Control Modules in Cyber-Physical Systems

Grobelna

2020

Sensors

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“…Göllner et al (2021) generated the dynamic simulation model automatically based on the standardized and interoperable information model where all necessary data is mapped into the AAS structure. The role of AASs in life cycle management (Marcon et al, 2019;Deuter and Imort, 2020;Rauh et al, 2022) This section provides findings from a comprehensive investigation of a previous case study conducted by one of the authors in Haghshenas et al (2023). The case study is based on the Hywind Tampen floating wind farm project, developed by Equinor, which aimed to implement a digital twin in offshore wind farms.…”

Section: The Interoperable Digital Twin Framework For the Offshore Wi...mentioning

confidence: 99%

Industry 4.0 Digital Twins in Offshore Wind Farms

Ambarita,

Karlsen,

Scibilia

et al. 2023

Preprint

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Abstract. The use of digital twins in offshore wind farms presents a major opportunity to advance autonomous operations and optimize productivity. By creating virtual replicas of physical assets and systems, digital twins allow for real-time monitoring, predictive maintenance, and efficient decision-making. In the context of Industry 4.0, wind turbines are required not only to be remotely monitored and controlled through real-time data during operation, but also to manage the whole life cycle within the entire value chain. To accomplish this, the implementation of digital twin frameworks in accordance with Industry 4.0 standards is crucial. Motivated by the advanced technologies related to industrial digital twins in the context of Industry 4.0 implemented in the manufacturing sector, this paper presents findings from a study investigating the potential for transferring knowledge of industrial digital twins to offshore wind farm context. To gain a deeper understanding of the digital twin concept in the context of offshore wind applications, we conducted extensive literature studies. Specifically, we examined frameworks used in both the manufacturing industry and offshore wind industry. Our goal is to identify commonalities and differences between these frameworks, and to determine how they could be adapted to the unique requirements of the offshore wind sector. The Asset Administration Shell (AAS), which is a key concept of the Reference Architecture Model for Industry 4.0 (RAMI 4.0), provides a standardized and modular approach to representing and managing assets in industrial systems. By adopting AAS, offshore wind farms could improve the scalability, adaptability, and interoperability of their digital twin systems, and also enable more efficient and effective operation and maintenance of the turbines. Based on our findings, we concluded that implementing the AAS could be a promising option for enhancing the functionality of digital twins in offshore wind farms, and for achieving interoperability in line with the standards of Industry 4.0.

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“…The availability of manufacturing resources is based on the combination of numbers and sequences and the performance of machine reliability. The simulation model [41][42][43][44][45] in the manufacturing process is used to explain each stochastic variable's behavior for the objective of productivity. This model evaluates the manufacturing performance of acute resources, failure, and repair requests.…”

Section: Machine Learning In Manufacturingmentioning

confidence: 99%

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

Shahbazi

Byun

2021

Applied Sciences

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The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

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New Approaches to Implementing the SmartJacket into Industry 4.0 ‡

Cited by 15 publications

References 21 publications

Formal Verification of Control Modules in Cyber-Physical Systems

Formal Verification of Control Modules in Cyber-Physical Systems

Industry 4.0 Digital Twins in Offshore Wind Farms

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

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