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Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing...
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Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing Industries

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 20657

Special Issue Editors

Dr. Ljiljana Stojanovic

E-Mail Website
Guest Editor
Fraunhofer IOSB, Germany
Interests: Industry 4.0; digital twins; knowledge engineering; IIoT architectural standards
Dr. Arne Jørgen Berre

E-Mail Website
Guest Editor
SINTEF Digital, SINTEF AS
Interests: Big Data; AI; interoperability; ICT standards; reference architectures

Special Issue Information

Dear Colleagues,

Digital twins are emerging today as a popular technology approach in many industries. A digital twin is generally considered a digital replica of a physical system that captures attributes and behaviors of that system. A digital twin is typically materialized as a set of isolated models that are either empirical or first-principles-based. A digital twin made of a combination of multiple models is often called a hybrid digital twin. The scope and impact of digital twins could be substantially increased with the incorporation of properties we usually associate with cognition, such as reasoning, planning, and learning.

Cognitive twins represent the next step in the evolution of the digital twin concept by including the cognitive properties to effectively deal with unforeseen situations. They will revolutionize digital twins not only by intertwining different models to achieve higher predictive capabilities but also by incorporating expert knowledge to find new answers to emerging questions. By combining human tacit knowledge with the power of digital twin models, better reactions will be enabled in situations where, when tackling the problem alone, neither human nor digital twin models can perform well without interactions.

The present Special Issue intends to explore new directions in the field of digital twins in combination with cognitive computing and to clarify the underlying reasons and benefits. The objective will be to document the current state-of-the-art, identify future directions, and compare and contrast various perspectives on using cognitive technologies for digital twins. Experimental studies and technical challenges characterized by innovative cognitive aspects are welcome. This Special Issue will also examine the industrial applications and implementation of cognitive digital twin technology in process and manufacturing industries. It will be particularly useful to learn of the approaches which have been undertaken by the industry in dealing with issues such as uncertainty, synergy between physical and data-driven models, collaborative aspects, etc.

Dr. Ljiljana Stojanovic
Dr. Arne Jørgen Berre
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Formal representation of digital twins
  • Lifecycle management of digital twins
  • AI-enhanced digital twins
  • Cognitive computing
  • Cognitive architecture
  • Acquisition, extraction, formalization, and usage of human knowledge for digital twins
  • Self-awareness and self-improvement of digital twins
  • Integrative learning and reasoning for digital twins
  • Industrial knowledge graphs
  • Digital twins for system of systems
  • Industry 4.0
  • Use cases from manufacturing and process industry

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Published Papers (4 papers)

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Research

15 pages, 902 KiB  
Article
Cognitive Digital Twins for Resilience in Production: A Conceptual Framework
by Pavlos Eirinakis, Stavros Lounis, Stathis Plitsos, George Arampatzis, Kostas Kalaboukas, Klemen Kenda, Jinzhi Lu, Jože M. Rožanec and Nenad Stojanovic
Information 2022, 13(1), 33; https://doi.org/10.3390/info13010033 - 12 Jan 2022
Cited by 28 | Viewed by 4695
Abstract
Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in [...] Read more.
Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs. Full article
(This article belongs to the Special Issue Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing Industries)
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15 pages, 2529 KiB  
Article
Impact on Inference Model Performance for ML Tasks Using Real-Life Training Data and Synthetic Training Data from GANs
by Ulrike Faltings, Tobias Bettinger, Swen Barth and Michael Schäfer
Information 2022, 13(1), 9; https://doi.org/10.3390/info13010009 - 28 Dec 2021
Cited by 2 | Viewed by 3171
Abstract
Collecting and labeling of good balanced training data are usually very difficult and challenging under real conditions. In addition to classic modeling methods, Generative Adversarial Networks (GANs) offer a powerful possibility to generate synthetic training data. In this paper, we evaluate the hybrid [...] Read more.
Collecting and labeling of good balanced training data are usually very difficult and challenging under real conditions. In addition to classic modeling methods, Generative Adversarial Networks (GANs) offer a powerful possibility to generate synthetic training data. In this paper, we evaluate the hybrid usage of real-life and generated synthetic training data in different fractions and the effect on model performance. We found that a usage of up to 75% synthetic training data can compensate for both time-consuming and costly manual annotation while the model performance in our Deep Learning (DL) use case stays in the same range compared to a 100% share in hand-annotated real images. Using synthetic training data specifically tailored to induce a balanced dataset, special care can be taken concerning events that happen only on rare occasions and a prompt industrial application of ML models can be executed without too much delay, making these feasible and economically attractive for a wide scope of industrial applications in process and manufacturing industries. Hence, the main outcome of this paper is that our methodology can help to leverage the implementation of many different industrial Machine Learning and Computer Vision applications by making them economically maintainable. It can be concluded that a multitude of industrial ML use cases that require large and balanced training data containing all information that is relevant for the target model can be solved in the future following the findings that are presented in this study. Full article
(This article belongs to the Special Issue Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing Industries)
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14 pages, 16799 KiB  
Article
A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data
by Şahan Yoruç Selçuk, Perin Ünal, Özlem Albayrak and Moez Jomâa
Information 2021, 12(10), 386; https://doi.org/10.3390/info12100386 - 22 Sep 2021
Cited by 6 | Viewed by 5044
Abstract
Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in [...] Read more.
Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area. Full article
(This article belongs to the Special Issue Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing Industries)
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15 pages, 3603 KiB  
Article
An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes
by Michael Jacoby, Branislav Jovicic, Ljiljana Stojanovic and Nenad Stojanović
Information 2021, 12(6), 217; https://doi.org/10.3390/info12060217 - 21 May 2021
Cited by 22 | Viewed by 4909
Abstract
Digital twins (DTs) are digital representations of assets, capturing their attributes and behavior. They are one of the cornerstones of Industry 4.0. Current DT standards are still under development, and so far, they typically allow for representing DTs only by attributes. Yet, knowledge [...] Read more.
Digital twins (DTs) are digital representations of assets, capturing their attributes and behavior. They are one of the cornerstones of Industry 4.0. Current DT standards are still under development, and so far, they typically allow for representing DTs only by attributes. Yet, knowledge about the behavior of assets is essential to properly control and interact with them, especially in the context of industrial production. This behavior is typically represented by multiple different models, making integration and orchestration within a DT difficult to manage. In this paper, we propose a new approach for hybrid DTs by intertwining different DT models. We also show how to realize this approach by combining the Fraunhofer Asset Administration Shell (AAS) Tools for Digital Twins (FAST) to create Industry 4.0-compliant DTs with Apache StreamPipes to implement and manage multiple DT models. Our prototype implementation is limited to a subset of the AAS metamodel and pull-based communication between FAST and an external Apache StreamPipes instance. Future work should provide full support for the AAS metamodel, publish/subscribe-based communication, and other execution environments as well as deployment strategies. We also present how this approach has been applied to a real-world use case in the steel production industry. Full article
(This article belongs to the Special Issue Cognitive Digital Twins: Challenges and Opportunities for Process and Manufacturing Industries)
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