Lifetime Learning-enabled Modelling Framework for Digital Twin

Yang, Chunsheng; Ferdousi, Rahatara; Saddik, Abdulmotaleb El; Li, Yifeng; Liu, Zheng; Liu, Min

doi:10.1109/case49997.2022.9926693

Cited by 5 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lou et al [ 148 , 149 ], Dashkina et al [ 202 ], as well as Tarkhov and Malykhina [ 203 ] used neural networks as behavioural models of a Digital Twin. Liu et al and Yang et al further used transfer learning approaches to adapt the models to changing conditions, for example, and thus increase robustness [ 204 , 205 ]. Zheng and Ni [ 142 ] also used real data to retrain the parameters of their model, creating a hybrid model.…”

Section: Resultsmentioning

confidence: 99%

A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems

Fett,

Wilking,

Goetz

et al. 2023

Sensors

View full text Add to dashboard Cite

Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins.

show abstract

Section: Resultsmentioning

confidence: 99%

A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems

Fett,

Wilking,

Goetz

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…• The lifetime learning capability is key to the success of a digital twin. 43 The physics-based models are limited by people's knowledge about the complexity of the physical assets and processes. The data-driven models also have constraints due to the parameter space covered by the available data.…”

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

Digital twin for predictive maintenance

Liu¹,

Blasch

Liu

et al. 2023

NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE

View full text Add to dashboard Cite

Digital twin engineering is a disruptive technology that creates a living data model of industrial assets. The living model will continually adapt to changes in the environment or operations using real-time sensory data as well as forecast the future of the corresponding infrastructure. A digital twin can be used to proactively identify potential issues with its real physical counterpart, allowing the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, and machine learning. This paper will review the digital twin technology and highlight its application in predictive maintenance applications.

show abstract

“…• Data Driven Framework: A data-driven DT can be created using machine learning and other AI techniques to learn from the data generated by the railway infrastructure. This can help in predicting potential issues, optimizing maintenance schedules, and improving overall performance [86] and [87]. • Data as an engineering tool: Data can be used as an engineering tool to model, simulate, and optimize the railway infrastructure [81].…”

Section: Tools For Data Collectionmentioning

confidence: 99%

“…(2022) [87] presented a framework for the development of such models, utilizing an AI-based DT to operate heavy freight cars. The authors applied ML models such as decision trees and Naive Bayes to two versions of the Wheel Impact Load Detector system (WILD), then employed Transfer Learning (TL) techniques to improve performance.…”

Section: ) Ai Approaches In Dtrmentioning

confidence: 99%

“…Therefore, to design and develop DTR, a seamless and continuous data stream is required. However, data distribution drift or acquiring data from outdated and sensor-lacking heavy freights has been a challenge for most studies [87]. Despite proposing transfer learning as a solution in contemporary research [87], the need for a continuous real-time data stream and access to railway industry data is essential to ensure the success of DTR.…”

Section: ) Data Availabilitymentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

show abstract

Lifetime Learning-enabled Modelling Framework for Digital Twin

Cited by 5 publications

References 12 publications

A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems

A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems

Digital twin for predictive maintenance

Digital Twin for Railway: A Comprehensive Survey

Contact Info

Product

Resources

About