Dynamic digital twin for predictive maintenance in flexible production systems

Barthelmey, André; Lee, Eunseo; Hana, Ramy; Deuse, Jochen

doi:10.1109/iecon.2019.8927397

Cited by 25 publications

(14 citation statements)

References 12 publications

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“…Additionally, some functions result from DT sub-components capabilities, technologies which are used to implement DT into a CPPS. For example, with the help of IIoT to provide real-time data transfer and data acquisition into a cloud-based depository and machine learning algorithms to analyze and detect anomalies, it is possible to implement DT to ensure predictive maintenance for production systems [28]. Earlier [29] defined a continuous production system evaluation and planning using automatic data acquisition.…”

Section: Dts In Smart Manufacturing Production Systemsmentioning

confidence: 99%

Digital Twin for Production Systems: A Literature Perspective

Pystina

Sekhari

Gzara

et al. 2022

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Dts In Smart Manufacturing Production Systemsmentioning

confidence: 99%

Digital Twin for Production Systems: A Literature Perspective

Pystina

Sekhari

Gzara

et al. 2022

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

View full text Add to dashboard Cite

show abstract

“…To support the predictive maintenance of a flexible production system, Barthelmey et al have proposed a dynamic DT [27]. The DT receives data in the AutomationML format, and the available analytical models are applied to the data to identify the predictive maintenance requirements.…”

Section: Dts and Dt-based Industry 40 Application Developmentmentioning

confidence: 99%

“…The DTs have the potential to support different aspects of Industry 4.0 application development. However, despite their potential in many cases, DTs are proposed to support specific applications [15,27,28]. Moreover, the lack of semantically rich machine descriptions hinders the ability to use the DTs in application development [29].…”

Section: Dts and Dt-based Industry 40 Application Developmentmentioning

confidence: 99%

“…Moreover, the lack of semantically rich machine descriptions hinders the ability to use the DTs in application development [29]. In addition, existing DTs have a limited ability to support the simulating/emulating complex machines including their machine automation [17,27,[29][30][31] or to allow switching between the physical machines and the simulators to support Industry 4.0 application testing [20,32].…”

Section: Dts and Dt-based Industry 40 Application Developmentmentioning

confidence: 99%

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Digital Twins Supporting Efficient Digital Industrial Transformation

Bamunuarachchi

Georgakopoulos

Banerjee

et al. 2021

Sensors

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Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

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“…This leads to wrong results in proven analysis models, because of the missing possibility to combine static and dynamic machine data. This underlines the need to combine up-to-date data and information with the content of technical documents in dedicated analysis models [20]. The combination of these different data sources becomes even more important when a DT is used across the ALC phases.…”

Section: Figure 1 the 6 Dimensions Of Dtmentioning

confidence: 99%

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

Passath

Huber

Biedermann

et al. 2021

Teh. glas. (Online)

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The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN).

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Dynamic digital twin for predictive maintenance in flexible production systems

Cited by 25 publications

References 12 publications

Digital Twin for Production Systems: A Literature Perspective

Digital Twin for Production Systems: A Literature Perspective

Digital Twins Supporting Efficient Digital Industrial Transformation

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

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