A RAMI 4.0 View of Predictive Maintenance: Software Architecture, Platform and Case Study in Steel Industry

Bousdekis, Alexandros; Lepenioti, Katerina; Ntalaperas, Dimitris; Vergeti, Danai; Apostolou, Dimitris; Boursinos, Vasilis

doi:10.1007/978-3-030-20948-3_9

Cited by 30 publications

(21 citation statements)

References 13 publications

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“…In the proposed architecture, descriptive analytics is implemented as real-time algorithms on the basis of sensor-generated data streams in order to detect abnormal behaviors. Business Layer: This layer ensures the integrity of functions in the value stream and enables mapping business models and the outcomes of the overall process (Bousdekis et al, 2019a). In other words, it assures the enterprise integration and interoperability by providing the interfaces with the rest of the business functions.…”

Section: Architecture Layersmentioning

confidence: 99%

“…Information Layer : This layer provides pre-processing of events and execution of event-related rules by enabling their formal description for the interpretation of the information (Bousdekis et al, 2019a ). It also manages data persistence and ensures consistent data integrity and transformation for feeding into the Functional Layer.…”

Section: The Proposed Architectural Framework For Big Data-driven Processes In Industry 40mentioning

confidence: 99%

“…Functional Layer : This layer enables the formal description of functions and creates the platform for horizontal integration of various functions (Bousdekis et al, 2019a ). It includes the run time and modeling environment for services supporting the business processes and a run time environment for applications and technical functionalities.…”

Section: The Proposed Architectural Framework For Big Data-driven Processes In Industry 40mentioning

confidence: 99%

“…Overall, in the literature, there are only a few case studies that follow the RAMI 4.0 model, and even fewer not requiring much effort to reach the level of practical implementation (Pisching et al, 2018 ). However, the key issue of any design and system development in the context of Industry 4.0 is the proper implementation of RAMI 4.0 in various manufacturing operations and the definition of appropriate sub-models for individual aspects and processes according to the technical background of Industry 4.0 (Zezulka et al, 2016 ; Moghaddam et al, 2018 ; Bousdekis et al, 2019a ). To this end, there is the need for architectural frameworks that will enable the systematic design and development of Industry 4.0 solutions so that they tackle the big data-rich, complex, and uncertain manufacturing environment in a holistic way.…”

Section: Introductionmentioning

confidence: 99%

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Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

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Section: Architecture Layersmentioning

confidence: 99%

Section: The Proposed Architectural Framework For Big Data-driven Processes In Industry 40mentioning

confidence: 99%

Section: The Proposed Architectural Framework For Big Data-driven Processes In Industry 40mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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“…At this stage, there remains lack of a coherent Predictive Maintenance Platform in the manufacturing industry, particularly with RAMI 4.0 and FIWARE. Several conceptual frameworks for predictive maintenance have been proposed in the research community [37,38,39,40,41,42,43]. The most recent approach proposes a Predictive Maintenance based on RAMI 4.0, subsequently provided a case study based on the proposed solution [43].…”

Section: Predictive Maintenancementioning

confidence: 99%

Predictive Maintenance in Industry 4.0

Sang

Vrieze

et al. 2020

Proceedings of the 10th International Conference on Information Systems and Technologies

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In the context of Industry 4.0, the manufacturing related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of Internet of things, i.e. smart devices and sensors increases availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0? Especially, applying RAMI4.0 architecture supports the predictive maintenance using FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions.

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Towards a Platform to Implement an Intelligent and Predictive Maintenance in the Context of Industry 4.0

Bourezza

Mousrij

2020

Advances in Intelligent Systems and Computing

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A RAMI 4.0 View of Predictive Maintenance: Software Architecture, Platform and Case Study in Steel Industry

Cited by 30 publications

References 13 publications

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Predictive Maintenance in Industry 4.0

Towards a Platform to Implement an Intelligent and Predictive Maintenance in the Context of Industry 4.0

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