An Integrated Platform for Multi-Model Digital Twins

Malakuti, Somayeh; Borrison, Reuben; Kotriwala, Arzam; Kloepper, Benjamin; Nordlund, Erik; Rönnberg, Kristian

doi:10.1145/3494322.3494324

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…As the operating environment of real industrial sensors is complex, full sample data needs to be quickly and effectively screened. Malakuti et al [25] solved the unstable data-dependent interference in machine learning systems by integrating DT models. Li et al [26] proposed that multi-sensor information fusion and error analysis are key in ensuring the accuracy of information extraction from the source.…”

Section: Multi-model Integration Fusion For Dt Evolution Modelmentioning

confidence: 99%

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

Bai,

Ren,

2023

Intelligent Automation &Amp; Soft Computing

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The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization. Secondly, a strong screening dual-model optimization (SSDO) prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism. Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model. Finally, taking the boiler performance of a power plant in Shanxi as an example, the accurate representation and evolution prediction of boiler steam quantity is realized. The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods. The self-lifting ability of the Stacking integration and fusionbased SSDO prediction method is 15.85% on average, and the optimal selflifting ability is 18.16%. The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074, and increases R 2 from the initial 0.731 to the optimal 0.9092. The adaptability and reliability of the model are comprehensively improved, and better prediction and analysis results are achieved. This ensures the stable operation of core equipment, and is of great significance to comprehensively understanding the equipment status and performance.

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Section: Multi-model Integration Fusion For Dt Evolution Modelmentioning

confidence: 99%

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

Bai,

Ren,

2023

Intelligent Automation &Amp; Soft Computing

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“…The implementation of DTs is expected "to greatly improve the management level of planning, design, construction, operation, and safety" [10]. A particular benefit that is often stressed in literature is the potential of DTs to integrate data from heterogeneous data sources [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin System for Railway Infrastructure

Doubell,

Basson,

Kruger

et al. 2023

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This paper presents a concept and case study implementation for a system of digital twins (DTs) aimed at supporting the management of railway infrastructure maintenance. The system of DTs includes DTs and software services. The case study uses, as physical asset, a subset of the railway infrastructure managed by the Passenger Rail Agency of South Africa (PRASA). The DT system concept is evaluated, using a case study, to assess its ability to meet users' information requirements and contextual requirements. The paper also evaluates the generality of the case study results. The case study illustrates that the DT system can acquire and integrate data from diverse data sources and be adapted to a changing context (in terms of user requirements, infrastructural elements, and data sources). Even though the case study considers only a subset of infrastructure, the scalability of the DT system is illustrated, indicating its potential for larger implementations, within and beyond PRASA. The paper, therefore, shows that the DT system concept can provide an integrated view of the data on the railway infrastructure, thereby supporting data-led decision-making by infrastructure maintenance managers.

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