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Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .ORCID iD: 0000-0002-3743-3710
TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .ORCID iD: 0000-0002-4107-0991
2021 (English)In: International Journal of Hydromechatronics, ISSN 2515-0464, Vol. 4, no 3Article in journal (Refereed) Published
Abstract [en]

This paper proposes a physics-based model which is part of a hybrid model (HyM). The physics-based model is developed for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage. This model will be used to generate data for building a data-driven mode. Thus, the combination of these two models provides the hybrid model-based approach (HyMAs). The physics-based model of the HVAC system is divided into four principal parts: cooling subsystems, heating subsystems, ventilation subsystems, and vehicle thermal networking. First, the subsystems are modelled, considering the sensors embedded in the real system. Next, the model is synchronised with the real system to give better simulation results and validate the model. The cooling subsystem, heating subsystem and ventilation subsystem are validated with the acceptable sum square error (SSE) results. Second, the new virtual sensors are defined in the model, and their value to future research is suggested

Place, publisher, year, edition, pages
InderScience Publishers, 2021. Vol. 4, no 3
Keywords [en]
physics-based modelling, hybrid modelling, digital twins, HVAC system, transportation engineering, signal validation, predictive maintenance, simulation, virtual sensor, fault detection
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-86167DOI: 10.1504/IJHM.2021.10034926ISI: 000704793800002OAI: oai:DiVA.org:ltu-86167DiVA, id: diva2:1575793
Note

Validerad;2021;Nivå 2;2021-10-13 (beamah)

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2022-11-10Bibliographically approved
In thesis
1. Hybrid digital twins: A co-creation of data science and physics
Open this publication in new window or tab >>Hybrid digital twins: A co-creation of data science and physics
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Safety is more important than reliability or efficiency in railway, aerospace, oil & gas, and chemical industries. Regulations are very restrictive in sectors where safety is paramount. This makes maintainers replace critical components in initial stages of degradation, which implies a loss of useful life and a lack of information about advanced stages of degradation for those components. Nevertheless, this lack of data can be overcome using hybrid digital twins, also known as hybrid-model based approaches (HyMAs), which combine data-driven models with physics-based models. This fusion minimizes the occurrence of undesirable failures that may interrupt the functionality of critical systems in a safe or cost-efficient manner.

HyMAs have been studied at Luleå University of Technology by other Ph.D. students who found promising direction for future research in prognostics and health management (PHM) applications. Thus, this research work continues the direction defined in previous research with the proposal of HyMAs for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage orientated to diagnostics and prognostics processes. The proposed hybrid modelling consists of the fusion of data obtained from two sources: data obtained from the real system and synthetic data generated by a developed physics-based model of the HVAC.

The HVAC system is considered a system of systems (SoS). Therefore, the physics-based model of the HVAC system is divided into four main systems: heating subsystem, cooling subsystem, ventilation subsystem, and cabin thermal networking subsystem. These subsystems are modelled considering the sensors installed in the real system and soft sensors, also known as virtual sensors, which provide crucial information for fault detection, diagnostics, and prognostics. These sensors defined in the physics-based model generate synthetic data which reproduce the behaviour of the system while a failure mode (FM) is simulated. Verification and validation are key processes to synchronise the response of the physics-based model with the signals obtained from the real system. Hence, the physics-based model is synchronised, verified, and validated using data collected by sensors located in the real system. These steps are conducted following guidelines suggested in the literature.

Different datasets containing real data and synthetic data while the HVAC system works in faulty and healthy states are used to train data-driven models for fault detection and diagnostics and to train data-driven models for prognostics.

Statistical features, such as shape factor, kurtosis, skewness, and sum square error, among others, are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data obtained from the real system. The data fusion is classified according to the condition indicators of the system in terms of FMs and level of degradation. The merged features are used to train data-driven models for fault detection and diagnostics. In addition, the real data can be loaded to the physics-based model to predict the degradation of the air filter.

Then, the prediction data are loaded to an exponential model that provides an estimation of the remaining useful life (RUL) of the air filter. To improve the prognostics model, the physics-based model is used to generate run-to-failure data which are used to train and test a deep convolutional neural network (CNN) which accurately estimates the RUL of the air filter.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022. p. 196
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
hybrid modelling, physics-based model, data-driven model, HVAC system, Railway
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93636 (URN)978-91-8048-190-8 (ISBN)978-91-8048-191-5 (ISBN)
Public defence
2022-12-07, F232, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2022-10-18 Created: 2022-10-18 Last updated: 2022-11-16Bibliographically approved

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Galvez, AntonioGalar, Diego

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