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Hybrid digital twins: A co-creation of data science and physics
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-3743-3710
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 [en]
hybrid modelling, physics-based model, data-driven model, HVAC system, Railway
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-93636ISBN: 978-91-8048-190-8 (print)ISBN: 978-91-8048-191-5 (electronic)OAI: oai:DiVA.org:ltu-93636DiVA, id: diva2:1704428
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
List of papers
1. Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway
Open this publication in new window or tab >>Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway
Show others...
2021 (English)In: Simulation Notes Europe, ISSN 2306-0271, Vol. 31, no 3, p. 121-126Article in journal (Refereed) Published
Abstract [en]

Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway’s maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers. 

Place, publisher, year, edition, pages
ARGESIM Publisher, 2021
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93601 (URN)10.11128/sne.31.tn.10572 (DOI)
Conference
10th EUROSIM Congress on Modelling and Simulation (EUROSIM 2019), Logroño, La Rioja, Spain, July 1-5, 2019
Note

Godkänd;2022;Nivå 0;2022-10-14 (hanlid);Konferensartikel i tidskrift

Available from: 2022-10-13 Created: 2022-10-13 Last updated: 2022-10-18Bibliographically approved
2. Synthetic Data Generation in Hybrid Modelling of Railway HVAC System
Open this publication in new window or tab >>Synthetic Data Generation in Hybrid Modelling of Railway HVAC System
2020 (English)In: 17th IMEKO TC 10 and EUROLAB Virtual Conference: “Global Trends in Testing, Diagnostics & Inspection for 2030” / [ed] Zsolt János Viharos; Lorenzo Ciani; Piotr Bilski; Mladen Jakovcic, International Measurement Confederation (IMEKO) , 2020, p. 79-84Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a hybrid model (HyM)for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC.

The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated.

Statistical features 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 from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy.

Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.

Place, publisher, year, edition, pages
International Measurement Confederation (IMEKO), 2020
Keywords
Predictive maintenance, fault detection, railway, hybrid modelling, fault modelling, synthetic data
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-81659 (URN)2-s2.0-85097716905 (Scopus ID)
Conference
17th IMEKO TC 10 and EUROLAB Virtual Conference, Online, October 20-22, 2020
Note

Finanssiär: Basque Government (KK-2020/0004);

ISBN för värdpublikation: 978-92-990084-6-1

Available from: 2020-11-27 Created: 2020-11-27 Last updated: 2022-10-18Bibliographically approved
3. Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
Open this publication in new window or tab >>Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
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
Keywords
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:nbn:se:ltu:diva-86167 (URN)10.1504/IJHM.2021.10034926 (DOI)000704793800002 ()
Note

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

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2022-11-10Bibliographically approved
4. Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
Open this publication in new window or tab >>Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
2021 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 12, article id 6828Article in journal (Refereed) Published
Abstract [en]

Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
fault detection, fault modelling, hybrid modelling, predictive maintenance, railway, HVAC systems, synthetic data, soft sensing
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-86165 (URN)10.3390/su13126828 (DOI)000666372800001 ()2-s2.0-85108878146 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-07-06 (alebob);

Finansiär: Basque Government (KK-2020/00049) 

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2022-10-18Bibliographically approved
5. A Hybrid Model-Based Approach on Prognostics for Railway HVAC
Open this publication in new window or tab >>A Hybrid Model-Based Approach on Prognostics for Railway HVAC
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 108117-108127Article in journal (Refereed) Published
Abstract [en]

Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Prognostics and health management, hybrid modelling, deep learning, HVAC system, railway
National Category
Vehicle Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93717 (URN)10.1109/ACCESS.2022.3211258 (DOI)000870212300001 ()2-s2.0-85140811123 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-07 (joosat);

Available from: 2022-10-25 Created: 2022-10-25 Last updated: 2022-11-10Bibliographically approved

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