Anagrafe della ricerca

Heating Ventilation and Air-Conditioning (HVAC) systems equipped with air-handling units (AHUs) are frequently operated in faulty conditions due to lack of proper maintenance, failure of components or incorrect installation. Faulty operation in AHUs leads to uncomfortable indoor environment, poor indoor air quality and significant wastes of energy and money. To this purpose, Fault Detection and Diagnosis (FDD) processes make it possible to automatically recognize fault occurrence and identify the causes and the location of fault, contributing to enhance both energy efficiency and indoor environmental quality. This project mainly aims to develop an automated, open, scalable and transparent FDD process for AHUs based on a hybrid expert and artificial intelligence-based approach. The project will start by creating a reference dataset based on experimental campaigns characterized by high resolution measurements of both normal and faulty operation of a typical existing monitored AHU (serving a 4x4x3.6 m3 test room) under different modes and weather/load conditions. The experimental dataset will represent a fundamental source of knowledge for assessing the real impact of several typical faults in terms of operating cost, energy consumption, GHG emissions and indoor comfort/air quality. Moreover, the dataset will be exploited to validate a digital twin capable to mimic the operation of the AHU in both faulty and normal conditions; the simulation model will make it possible to conduct robust fault impact scenarios and to extend the operating ranges of training measured data. Both the experimental and simulation datasets will be made publicly available on a data repository well-recognised by researchers, thus opening the opportunity for the scientific community to perform replicability and benchmark studies on FDD processes for AHUs. Novel hybrid FDD strategies including both data-driven and knowledge-based models will be then developed based on the obtained datasets. The hybrid FDD framework will make it possible to exploit the potentialities of physics-based models for the description and interpretation of faults occurrence and artificial intelligence techniques to extract non-trivial knowledge from experimental and simulated data. Finally, the transferability and scalability of the conceived FDD strategy, exploiting ontology schema and applying a transfer learning framework with reference to a target AHU different from the one used for the development of the FDD strategy itself will be tested. The project will represent a cutting-edge experience thanks to the proposed holistic approach aiming to the resolution of the main challenging issues in the field of FDD for AHUs. The flow of activities can be replicated also for other AHUs with the aim of supporting an easier penetration of advanced automatic FDD tools in the automation industry as a key and low-cost solution to enhance energy management in buildings.