Heating, Ventilation and Air-Conditioning (HVAC) systems equipped with Air-Handling Units (AHUs) significantly contribute to the overall energy demand of the building sector and they frequently operate under faulty conditions due to lack of proper maintenance, components’ failure or incorrect installation. Automated Fault Detection and Diagnosis (AFDD) allows to automatically recognize faults occurrence as well as identify causes and/or location of faults. In comparison to traditional maintenance programs, it can offer several interrelated benefits, including energy savings, operating cost reduction, lower greenhouse gas emissions and improved comfort. In particular, data-driven AFDD tools are easier to be utilized and could achieve higher accuracy with respect to other AFDD approaches, but they rely on operational data collected from AHUs operating under both normal and faulty conditions. However, performing experiments to derive such data is challenging, time-consuming, expensive, and it allows to cover limited ranges of operating conditions. An effective alternative is represented by the exploitation of AHU simulation models, which facilitate the generation of broad faulty operational datasets while exploring different AHU configurations/sizes/control logics and wide weather/loads scenarios. In this paper the most significant scientific papers focusing on the development of digital twins of AHUs aiming at the development of data-driven AFDD algorithms are systematically reviewed and categorized in terms of AHU operating schemes, software environments, faults’ type and severity, simulation time-step, range of climatic conditions. This review has been performed in order to highlight the current research gaps and guide the future development of new and accurate AHU simulation models to perform fault impact scenario analyses as well as support the deployment of data-driven AFDD methods. The analysis highlighted that: (i) dual-duct dual-fan air variable volume AHUs have been poorly modelled and simulated; (ii) the most used software platform for modelling and simulating AHUs’ operation is EnergyPlus; (iii) faults related to the humidifier have not been enough studied; (iv) the effects associated to the simultaneous occurrence of multiple faults have been assessed in a few cases; (v) performance analyses of AHUs under shoulder seasons and Mediterranean climatic conditions require further researches.

Critical Review of Studies on Digital Twins of Air-Handling Units for Data-Driven Fault Detection and Diagnosis Methods

Antonio ROSATO
;
Mohammad EL YOUSSEF;Francesco GUARINO;Antonio CIERVO;Sergio SIBILIO
2022

Abstract

Heating, Ventilation and Air-Conditioning (HVAC) systems equipped with Air-Handling Units (AHUs) significantly contribute to the overall energy demand of the building sector and they frequently operate under faulty conditions due to lack of proper maintenance, components’ failure or incorrect installation. Automated Fault Detection and Diagnosis (AFDD) allows to automatically recognize faults occurrence as well as identify causes and/or location of faults. In comparison to traditional maintenance programs, it can offer several interrelated benefits, including energy savings, operating cost reduction, lower greenhouse gas emissions and improved comfort. In particular, data-driven AFDD tools are easier to be utilized and could achieve higher accuracy with respect to other AFDD approaches, but they rely on operational data collected from AHUs operating under both normal and faulty conditions. However, performing experiments to derive such data is challenging, time-consuming, expensive, and it allows to cover limited ranges of operating conditions. An effective alternative is represented by the exploitation of AHU simulation models, which facilitate the generation of broad faulty operational datasets while exploring different AHU configurations/sizes/control logics and wide weather/loads scenarios. In this paper the most significant scientific papers focusing on the development of digital twins of AHUs aiming at the development of data-driven AFDD algorithms are systematically reviewed and categorized in terms of AHU operating schemes, software environments, faults’ type and severity, simulation time-step, range of climatic conditions. This review has been performed in order to highlight the current research gaps and guide the future development of new and accurate AHU simulation models to perform fault impact scenario analyses as well as support the deployment of data-driven AFDD methods. The analysis highlighted that: (i) dual-duct dual-fan air variable volume AHUs have been poorly modelled and simulated; (ii) the most used software platform for modelling and simulating AHUs’ operation is EnergyPlus; (iii) faults related to the humidifier have not been enough studied; (iv) the effects associated to the simultaneous occurrence of multiple faults have been assessed in a few cases; (v) performance analyses of AHUs under shoulder seasons and Mediterranean climatic conditions require further researches.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/480749
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