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He holds his bachelor’s degree in Mechanical Engineering from the University of Malaga (UMA) in 2016, his master’s degree in Industrial Engineering from Deusto University in 2018, he develops the related master thesis in DeustoTech research center in the area of energy and environment. He also holds his master’s degree in Industrial Management Engineering from Deusto University in 2019, whose master thesis was developed in ZF Chassis Components Toluca, Toluca de Lerdo (Mexico). Since January 2019, he is an industrial PhD student in the Division of Operation and Maintenance at Lulea University of Technology (LTU) and develops his doctoral thesis in an industrial environment in the area of Reliability and Maintenance of the industry and transport division at Tecnalia. The topic of his doctoral thesis is conduced to extend the useful life of critical components and systems by researching and applying novel techniques. He has publications related to his doctoral thesis in various journals and internationals conferences. He has been a guest professor at University of La Rioja (UNIR), where was part of the opponent board of different master thesis.
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.
Antonio Gálvez; Alberto Diez-Olivan; Dammika Seneviratne; Diego Galar. Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach. Sustainability 2021, 13, 6828 .
AMA StyleAntonio Gálvez, Alberto Diez-Olivan, Dammika Seneviratne, Diego Galar. Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach. Sustainability. 2021; 13 (12):6828.
Chicago/Turabian StyleAntonio Gálvez; Alberto Diez-Olivan; Dammika Seneviratne; Diego Galar. 2021. "Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach." Sustainability 13, no. 12: 6828.
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.
Antonio Gálvez; Dammika Seneviratne; Diego Galar. Hybrid Model Development for HVAC System in Transportation. Technologies 2021, 9, 18 .
AMA StyleAntonio Gálvez, Dammika Seneviratne, Diego Galar. Hybrid Model Development for HVAC System in Transportation. Technologies. 2021; 9 (1):18.
Chicago/Turabian StyleAntonio Gálvez; Dammika Seneviratne; Diego Galar. 2021. "Hybrid Model Development for HVAC System in Transportation." Technologies 9, no. 1: 18.