Description Means for Information Artifacts Throughout the Life Cycle of CPPS

Lüder, Arndt; Schmidt, Nicole; Hell, Kristofer; Röpke, H.; Zawisza, Jacek

doi:10.1007/978-3-319-56345-9_7

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…Connection to control layer, as performed in other works [54], could be potentially addressed in an extension of the current study. Along the same lines, KPIs related to CPPS issues [55] could potentially extend the applicability of the current framework. Finally, regarding innovation and technology readiness, the upscale of technologies that are integrated in shop-floor is also relevant, in terms of decision making for designing systems and process planning integrating Industry 4.0 Key Enabling Technologies.…”

Section: Appl Sci 2020 10 X 12 Of 16mentioning

confidence: 91%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

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A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.

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Section: Appl Sci 2020 10 X 12 Of 16mentioning

confidence: 91%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

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“…PSE is a multi-disciplinary process that involves various disciplines, like mechanical, electrical, and software engineering [11]. Engineering teams iteratively perform tasks, like mechanical design or implementation of the control software [13], to engineer the desired production system.…”

Section: A Production Systems Engineeringmentioning

confidence: 99%

“…The engineering of a production system is a collaborative effort of experts coming from many disciplines, like mechanical, electrical, and software engineering [11]. However, traditionally much of the engineering information is hidden in scattered engineering artifacts and much of the knowledge is implicit domain knowledge of engineering experts [5] (cf.…”

Section: Introductionmentioning

confidence: 99%

Patterns for Reuse in Production Systems Engineering

Meixner¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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In Production Systems Engineering (PSE), domain experts aim at reusing production processes implemented as Industry 4.0 assets and software. However, the knowledge on reusable assets is often scattered on multi-disciplinary engineering artifacts and domain experts, making it hard to find suitable reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for reuse in PSE based on a domain analysis; (ii) introduce the Industry 4.0 Asset Network (I4AN) that integrates multi-disciplinary dependencies between the assets and exposes recurring patterns; and (iii) present four patterns for reuse in PSE that aim at improving reuse efficiency and risk. We evaluate the I4AN with reuse scenarios in a feasibility study. The study results indicate that the I4AN model satisfies the elicited requirements and enables PSE domain experts to identify patterns for reuse in their contexts.

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