Synthetic Data Generation in Hybrid Modelling of Railway HVAC System

Antonio Gálvez, Alberto Diez-Olivan, Dammika Seneviratne, Diego Galar

Abstract

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.

Keywords

Predictive maintenance, fault detection, railway, hybrid modelling, fault modelling, synthetic data

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IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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