Digital Twins Development and Deployment

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Manufacturing Technology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 3323

Special Issue Editor


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Guest Editor
Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics, Luleå University of Technology, Luleå, Sweden
Interests: prognostics; servitization; digitization; MaaS; eMaintenance; diagnostics; supportability; hybrid models

Special Issue Information

Dear Colleagues,

The technology and operation of assets are complex, but the adoption of IoT in and its use with OT platforms enable the use of ‘digital twins’ to manage, monitor, and maintain assets. The digital twin connects complex assets and their OT systems to an IT environment by capturing data to monitor performance, deterioration and failure, location and safety compliance, and remote monitoring systems for scheduling and asset utilization.

Through data fusion, digital twins become virtual and digital representations of physical entities or systems. However, the clone created with IT and OT convergence to forecast failures, demand, customer behavior, or degradation of assets is not complete since it lacks engineering knowledge. This happens because the digital engineering models developed during the engineering phase of projects do not typically play a role in the operational phase.

Therefore, digital transformation demands that engineering technology (ET) be included in the IT/OT convergence process as the importance of integrating product design increases. For that purpose, digital twins must be complemented by other information to assess the overall condition of the whole fleet/system, including information from design and manufacturing, as this contains the physical knowledge of assets.

Prof. Dr. Diego Galar
Guest Editor

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Published Papers (1 paper)

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16 pages, 3284 KiB  
Article
Hybrid Model Development for HVAC System in Transportation
by Antonio Gálvez, Dammika Seneviratne and Diego Galar
Technologies 2021, 9(1), 18; https://doi.org/10.3390/technologies9010018 - 5 Mar 2021
Cited by 4 | Viewed by 2847
Abstract
Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and [...] Read more.
Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models. Full article
(This article belongs to the Special Issue Digital Twins Development and Deployment)
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