Browsing by Author "Gálvez, Antonio"
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Item Acoustic emission characterisation of two pre-cracked specimens(British Institute of Non-Destructive Testing, 2022) Gálvez, Antonio; Galar, Diego; Alonso, Asier; Errasti-Alcalá, Borja; Bienvenido, Ismael; Ortego, Patxi; Juuso, Esko; Tecnalia Research & InnovationThis article contains the experiments carried-out to study the capabilities of Acoustic Emissions (AE) in a Ship To Shore (STS) crane. This solution studies the implementation of Structural Health Monitoring (SHM) in an STS crane based on acoustic emissions (AE) technique for detecting cracks and assessing their growth in steel elements subjected to fatigue. The first experiment is performed using a compact tension specimen (CT) made of steel S355 whose dimensions are 125x120x50 mm and its cracks and dimensions are defined based on ASTM and ISO standards. The CT is monitored using AE sensors, and then, the features are extracted from the raw data and used to train, test and validate an unsupervised model. The crack detection model obtains a remarkable accuracy; crack detection at sizing of 3 mm length. As the CT dimensions are small, it is difficult to evaluate the attenuation of AE signals, which is completely necessary for monitoring STS cranes. Therefore, a second experiment is performed using a panel made of steel S355, whose dimensions are 2120x200x8 mm; the panel contains a crack of 50x3 mm. This experiment is performed to analyse the AE signals that come from cracks; specifically, to assess signals attenuation, how the attenuation affects cracks detection in the panel, and features evolution while crack propagation. This is led by monitoring the crack growth with crack detection gauges and installing the AE sensors at different distances of the crack. The assessment is used to develop an unsupervised model to detect cracks and an algorithm for localizing them.Item Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model(2021) Gálvez, Antonio; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSThis 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.Item Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach(2021-06-16) Gálvez, Antonio; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSHeating, 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.Item Feature Assessment for a Hybrid Model(Springer Science and Business Media Deutschland GmbH, 2023) Gálvez, Antonio; Seneviratne, Dammika; Galar, Diego; Juuso, Esko; Juuso, Esko; Galar, Diego; Tecnalia Research & InnovationThis paper proposes an assessment of features orientated to improve the accuracy of a hybrid model (HyM) used for detecting faults in a heating, ventilation, and air conditioning (HVAC) system. The HyM combines data collected by sensors embedded in the system with data generated by a physics-based model of the HVAC. The physics-based model includes sensors embedded in the real system and virtual sensors to represent the behaviour of the system when a failure mode (FM) is simulated. This fusion leads to improved maintenance actions to reduce the number of failures and predict the behaviour of the system. HyM can lead to improved fault detection and diagnostics (FDD) processes of critical systems, but multiple fault detection models are sometimes inaccurate. The paper assesses features extracted from synthetic signals. The results of the assessment are used to improve the accuracy of a multiple fault detection model developed in previous research. The assessment of features comprises the following: (1) generation of run-to-failure data using the physics-based model of the HVAC system; the FMs simulated in this paper are dust in the air filter, degradation of the CO2 sensor, degradation of the evaporator fan, and variations in the compression rate of the cooling system; (2) identification of the individual features that strongly distinguish the FM; (3) analysis of how the features selected vary when components degrade.Item Hybrid Model Development for HVAC System in Transportation(2021-03) Gálvez, Antonio; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSHybrid 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.Item Synthetic data generation in hybrid modelling of railway HVAC system(2020) Gálvez, Antonio; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar, Diego; Tecnalia Research & Innovation; INDUSTRY_THINGSThis 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.