Integrating heterogeneous engineering knowledge and tools for efficient industrial simulation model support

Novák, Petr; Serral, Estefanía; Mordinyi, Richard; Šindelář, Radek

doi:10.1016/j.aei.2015.05.001

Cited by 17 publications

(4 citation statements)

References 28 publications

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“…Simulation is carrying out a number of experiments. Making them directly on the real system would be time consuming and expensive, so that is why the preferred approach is to move these experiments into the simulated world [20]. Based on the method of discrete event simulation, which was applied in simulation software Simio, we were able to see the results of the project in the digital environment.…”

Section: Use Of Industrial Engineering Knowledge In the Medical Fieldmentioning

confidence: 93%

Knowledge in Healthcare

Gregor¹,

Grznár²,

Pedan³

et al. 2016

DAAAM Proceedings

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Authors of the article are dealing with the issue of the use of industrial engineering elements in the sphere of healthcare delivery and the issue of sharing knowledge alone. Both of these areas are highly popular subjects of today, and the effort of individual scientists is to merge them together. Article offers insight into global trends in this area and their evaluation. It also describes various benefits associated with these two spheres and the opportunities they offer. The final part consists of solutions that have been implemented in this field and visions for further direction in this area in our department.

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Section: Use Of Industrial Engineering Knowledge In the Medical Fieldmentioning

confidence: 93%

Knowledge in Healthcare

Gregor¹,

Grznár²,

Pedan³

et al. 2016

DAAAM Proceedings

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“…Semantic Web techniques have been used successfully for integrating disparate data in the design and operations lifecycle stages of oil and gas plants [40]- [42]. The Semantic Web uses the Resource Description Framework (RDF) [43] as a standard graph-based data model with globally defined identifiers and terminologies.…”

Section: Semantic Web Applicationmentioning

confidence: 99%

Data integration for offshore decommissioning waste management

Akinyemi

Sun

Gray

2020

Automation in Construction

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Offshore decommissioning represents significant business opportunities for oil and gas service companies. However, for owners of offshore assets and regulators, it is a liability because of the associated costs. One way of mitigating decommissioning costs is through the sales and reuse of decommissioned items. To achieve this effectively, reliability assessment of decommissioned items is required. Such an assessment relies on data collected on the various items over the lifecycle of an engineering asset. Considering that offshore platforms have a design life of about 25 years and data management techniques and tools are constantly evolving, data captured about items to be decommissioned will be in varying forms. In addition, considering the many stakeholders involved with a facility over its lifecycle, information representation of the items will have variations. These challenges make data integration difficult. As a result, this research developed a data integration framework that makes use of Semantic Web technologies and ISO 15926-a standard for process plant data integration-for rapid assessment of decommissioned items. The proposed solution helps in determining the reuse potential of decommissioned items, which can save on cost and benefit the environment.

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“…The Semantic Sensor Network (SSN) 2 ontology, developed at W3C 3 , is well suited to describe process states and their observability, as well as resource states. The Automation Ontology (AO) captures knowledge about industrial plants and their automation systems to support engineering simulation models [21]. AO concerns mechatronic concepts to support simulation model design and integration.…”

Section: B Knowledge Representation In Psementioning

confidence: 99%

Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

Biffl¹,

Kathrein²,

Lüder³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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In the multidisciplinary production systems engineering (PSE) process, software engineers depend on requirements and design rationales coming from product and production process planning, summarized as product/ion knowledge. Unfortunately, the engineering artifacts coming from product/ion planning often represent important product/ion knowledge incompletely and not well integrated, leading to risks regarding software engineering quality. In this paper, we report on a case study at a large industrial PSE organization, investigating Technical Debt (TD) effects, items, and causes in PSE process documentation and configuration management according to the VDI guideline 3695 Part 2. We focus on requirements for and issues in the representation of product/ion knowledge in the engineering data provided to software engineers. Based on data elicited from PSE domain experts, we model TD concepts based on the Quality Function Deployment method as foundation for TD analysis and risk management. The initial validation with domain experts revealed how software engineers could benefit from improved product/ion knowledge modeling as foundation for better understanding the rationale of engineering design decisions.

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Integrating heterogeneous engineering knowledge and tools for efficient industrial simulation model support

Cited by 17 publications

References 28 publications

Knowledge in Healthcare

Knowledge in Healthcare

Data integration for offshore decommissioning waste management

Software Engineering Risks from Technical Debt in the Representation of Product/ion Knowledge

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