Service Based Approach to Asset Administration Shell for Controlling Testing Processes in Manufacturing*

Rehman, Hamood Ur; Chaplin, Jack C.; Zarzycki, Leszek; Mo, Fan; Jones, Mark M.; Ratchev, Svetan

doi:10.1016/j.ifacol.2022.09.668

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Multi-agent system (MAS) in smart manufacturing is an emerging technology, which has not been widely used by manufacturers but has a high potential to develop a more autonomous and efficient system, especially in complex manufacturing processes is agent-based computation [38]. Agent-based technologies have attracted considerable interest in the research community due to their ability to tackle highly distributed and reconfigurable control systems [39]. These can help in developing a more dynamic and flexible manufacturing solution.…”

Section: Masmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

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Section: Masmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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“…The authors concluded that the implemented AASs were able to monitor the condition of the servo motor and detect the exceeded thresholds. Rehman et al (2022) implemented AAS in controlling the functionality of an intelligent testing process in the production system for small to medium enterprises (SMEs) which depend significantly on testing processes for their low volume but highly bespoke products. The implementation of the AAS involved observing the behavior change on the asset, thus directly controlling the behaviour of a production process.…”

Section: Digital Twin Framework In the Manufacturing Industrymentioning

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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“…Manufacturing data models represent manufacturing assets, such as processes, activities, and resources [1]. These models can be conceptual but are often utilised in manufacturing software applications, such as simulation tools, planning systems, and testing [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection of Manufacturing Configurations Using Object-Oriented and Mathematical Data Models

Torayev,

Wang,

Martínez-Arellano

et al. 2023

Advances in Transdisciplinary Engineering

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In this work, we optimize manufacturing configurations, i.e., the set of necessary assets, using the known capabilities and capacities of manufacturing equipment. In particular, the work provides an Object-Oriented data model and the translation of the Object-Oriented data model into a Mathematical data model for efficiently utilizing optimization algorithms. The main contribution is developing an optimization strategy for adapting to varying demand periods with the requested capability and capacity. The proposed methodology is validated in optimizing the planning for small-box hinged product assemblies in aerospace manufacturing, which can be assembled in a reconfigurable environment with common pick and place, drilling, fastening, and inspection procedures.

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Service Based Approach to Asset Administration Shell for Controlling Testing Processes in Manufacturing*

Cited by 9 publications

References 8 publications

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Industry 4.0 Digital Twins in Offshore Wind Farms

Optimal Selection of Manufacturing Configurations Using Object-Oriented and Mathematical Data Models

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