Risk minimization in modernization projects of plant automation &amp;#x2014; A knowledge-based approach by means of semantic web technologies

Strube, Martin; Runde, Stefan; Figalist, Helmut; Fay, Alexander

doi:10.1109/etfa.2011.6058987

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…The constraints are specified on the meta model of the modeling language and executed on the abstract syntax of the model, i.e., they concern only the static and not the dynamic semantics. A rule based approach to identify structural inconsistencies during evolution of aPS is presented in Strube et al (2011).…”

Section: Consistency Between Other System Design Aspectsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

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245

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a b s t r a c tCoping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

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Section: Consistency Between Other System Design Aspectsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

Self Cite

245

View full text Add to dashboard Cite

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“…The systems themselves have to enable the implementation of digital twins, i.e., their properties must be known or observable, as well as they have to provide high-quality data [ 82 ]. In particular, production and processing machines need to be upgradeable, which may lead to higher investment costs [ 115 ]. Further, there are studies on remotely food monitoring during distribution, retail, and consumption [ 116 , 117 ].…”

Section: Discussionmentioning

confidence: 99%

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Henrichs

Noack

Piedrahita

et al. 2021

Sensors

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The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

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“…This information is stored in a static knowledge model to avoid a repeatedly performed analysis of the dynamic models. This can be an extension of the information model or any other suitable representation form, like CAEX [20]. Such static knowledge models are not always necessary because the static information can be given implicitly within dynamic knowledge models; this is e.g.…”

Section: Using Models As Interlayer Between Signals and Operationalizmentioning

confidence: 98%

Evolution Management of Production Facilities by Semi-Automated Requirement Verification

Ladiges

Haubeck²,

Fay

et al. 2014

At - Automatisierungstechnik

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The concept described in this contribution utilizes available process data in production systems to enable to verify the fulfillment of (non-functional) requirements during operation. Therefore, the contribution presents a systematic approach to automatically derive property values out of signal traces by using adaptable runtime models. Those can be checked for the violation of limit values in order to verify the fulfillment or violation of requirements on these properties during an evolution. In an example application to a Pick and Place unit, the concept is used together with an anomaly detection method to support the operator during the evolution process by constantly providing information regarding requirement fulfillment.

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Risk minimization in modernization projects of plant automation — A knowledge-based approach by means of semantic web technologies

Cited by 13 publications

References 9 publications

Evolution of software in automated production systems: Challenges and research directions

Evolution of software in automated production systems: Challenges and research directions

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Evolution Management of Production Facilities by Semi-Automated Requirement Verification

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