Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Antonio Gálvez, Alberto Diez-Olivan, Dammika Seneviratne and Diego Galar
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Antonio Gálvez: TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain
Alberto Diez-Olivan: TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain
Dammika Seneviratne: TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain
Diego Galar: TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain

Sustainability, 2021, vol. 13, issue 12, 1-18

Abstract: 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.

Keywords: fault detection; fault modelling; hybrid modelling; predictive maintenance; railway; HVAC systems; synthetic data; soft sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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