An Introduction to OPC UA TSN for Industrial Communication Systems

Bruckner, Dietmar; Stãnicã, Marius-Petru; Blair, Richard E.; Schriegel, Sebastian; Kehrer, Stephan; Seewald, Maik; Sauter, Thilo

doi:10.1109/jproc.2018.2888703

Cited by 162 publications

(66 citation statements)

References 20 publications

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“…From the implementation perspective, some compatible standards proposed in the Industry 4.0 pyramid can be adopted. For instance, OPC UA is an open standard that specifies information exchange for industrial communication needs [101]. OPC UA covers all the layers from the bottom component level to the top plantwide decision level, including those in Figure 3.…”

Section: ) Resilient Control and Full Life-cycle Managementmentioning

confidence: 99%

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Jiang

Yin

Kaynak

2021

IEEE Open J. Ind. Electron. Soc.

107

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Section: ) Resilient Control and Full Life-cycle Managementmentioning

confidence: 99%

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Jiang

Yin

Kaynak

2021

IEEE Open J. Ind. Electron. Soc.

107

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“…On the other hand, industrial communication is expensive because the multitude of various technologies causes huge efforts of engineering, integration, and management. The advance of industrial technologies [ 50 ] towards converged communication infrastructure has benefited greatly from evolving industrial-grade technologies, such as OPC UA, Time-Sensitive Networking (TSN, a set of IEEE standards to provide deterministic networking [ 51 ]), and Ethernet-Advanced Physical Layer (Ethernet-APL, intrinsic safe wiring and communication).…”

Section: Key Aspects Of Industrial Cpsmentioning

confidence: 99%

Developing Industrial CPS: A Multi-Disciplinary Challenge

Hoffmann¹,

Malakuti²,

Grüner³

et al. 2021

Sensors

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Industrial Cyber–Physical System (CPS) is an emerging approach towards value creation in modern industrial production. The development and implementation of industrial CPS in real-life production are rewarding yet challenging. This paper aims to present a concept to develop, commercialize, operate, and maintain industrial CPS which can motivate the advance of the research and the industrial practice of industrial CPS in the future. We start with defining our understanding of an industrial CPS, specifying the components and key technological aspects of the industrial CPS, as well as explaining the alignment with existing work such as Industrie 4.0 concepts, followed by several use cases of industrial CPS in practice. The roles of each component and key technological aspect are described and the differences between traditional industrial systems and industrial CPS are elaborated. The multidisciplinary nature of industrial CPS leads to challenges when developing such systems, and we present a detailed description of several major sub-challenges that are key to the long-term sustainability of industrial CPS design. Since the research of industrial CPS is still emerging, we also discuss existing approaches and novel solutions to overcome these sub-challenges. These insights will help researchers and industrial practitioners to develop and commercialize industrial CPS.

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“…The communication system between sensors and databases must be implemented in order to allow for both real-time machine health monitoring and offline data handling. To this extent, a widely adopted solution by industries is the Open Platform Communication Unified Architecture (OPC UA), a popular communication protocol that allows information to be shared across sensors, industrial assets and the Cloud in a highly secure way ( Bruckner et al, 2019 ).…”

Section: Elements Of Prognostic and Health Managementmentioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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An Introduction to OPC UA TSN for Industrial Communication Systems

Cited by 162 publications

References 20 publications

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

Developing Industrial CPS: A Multi-Disciplinary Challenge

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

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