Ontology-Assisted Engineering of Cyber–Physical Production Systems in the Field of Process Technology

Engel, Grischan; Greiner, Thomas; Seifert, Sascha

doi:10.1109/tii.2018.2805320

Cited by 47 publications

(26 citation statements)

References 23 publications

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“…UML-based approaches can fully automate the generation process of the IIoT-compliant layer that is required for the cyber-physical components to be effectively integrated in the shop-floor [333]. In order to achieve rapid response to changes from both high-level control systems and plant environment, self-manageable ontological agents can improve flexibility and interoperability [337] and automate the process engineering using a knowledge-based assistance system [341]. IIoT gateways have already been integrated with dynamic and flexible rule-based control strategies [344].…”

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…Ontology has been used in production management to build up domain-specific knowledge-bases (e.g. in the automation domain (Legat et al 2014), human-robot collaboration on the shop floor (Ullrich 2016) and CPPS in the field of process technology (Engel, Greiner, and Seifert 2018)). Nevertheless, the problem-solving and learning from experiences (i.e.…”

Section: Description Of the Modelmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

142

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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“…In addition to the skill information, data on operation status (e.g., manufacturing progress, utilization, maintenance needs) and start/stop commands need to be exchanged. To match the skills offered by heterogeneous agents with production requirements, [10] propose the use of an ontology. This enables generating the production plan.…”

Section: Definitions and Related Workmentioning

confidence: 99%

Flexibility Enhancements in Digital Manufacturing by means of Ontological Data Modeling

Otrebski¹,

Rauer²,

Engelhardt‐Nowitzki³

et al. 2020

JUSPN

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Cyberphysical production systems are important parts of today's manufacturing process. The ever-growing need of highly optimized, i.e. at the same time flexible and efficient systems, requires the use of not only appropriate machines, but as well a communication framework and data model that is manufacturer independent and scalable. This paper proposes a communication-framework based on OPC UA that employs an agent-based architecture. The proposed system has been implemented and tested in the Digital Factory of the UAS Technikum Wien. It shows promising behavior within distributed manufacturing systems.

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Ontology-Assisted Engineering of Cyber–Physical Production Systems in the Field of Process Technology

Cited by 47 publications

References 23 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

PriMa: a prescriptive maintenance model for cyber-physical production systems

Flexibility Enhancements in Digital Manufacturing by means of Ontological Data Modeling

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