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.
Highlights
Sustainability is one of the driving forces of the twenty-first century
This paper presents a hybrid model-based approach (HyMA) for implementing fault detection and diagnostics (FDD) and prognostics processes
The experiments are performed using data that contain faults in different levels of degradation. They can appear individually or together, and they come from the different subsystems of a HVAC system that is installed in a high-speed passenger train carriage
Summary
Sustainability is one of the driving forces of the twenty-first century. The major drivers of sustainability are governance and regulation, customers’ requirements, environmental priorities, natural resource shortages, and increasing energy costs [1,2]. The diagnostic process in HVAC systems, through CBM, is the system and degradation. The diagnostic process in HVAC systems, through CBM, mainly implemented using data-driven models; a data-driven model for deploying fault is mainly implemented using data-driven models; a data-driven model for deploying detection and diagnostics processes for air handling units is presented in [11,12]. These fault detection and diagnostics processes for air handling units is presented in [11,12]. RUL estimation evaluates the accumulated assessing the changes in its behavior over time.
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