CAL4AHU - Calibration of air handling unit models for fault detection and diagnosis
Febres Pascual, Jesús
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A systematic methodology for the automated calibration of Air Handling Unit (AHU) computational models for use in Fault Detection and Diagnosis (FDD) is investigated that requires a minimum dataset and produces a trade-off between (model) accuracy and (calibration) complexity. The most relevant calibration methodologies for AHU models are reviewed, and one, based on physical models and machine learning techniques, is proposed. This methodology is implemented along with a first principle-based modelling library to produce a tool that facilitates the automation of the workflow from AHU system modelling and simulation to the model calibration. The proposed methodology is demonstrated in a real AHU located at roof level on the fifth floor in Cork School of Music, in Ireland as part of the IERC-funded EMWiNS project. The main contribution of this thesis to the EMWiNS project was to develop and provide accurate calibrated computational models of the psychrometric processes occurring in the AHU specifically focusing on temperature changes. These computational models enabled model-based FDD which was compared with a standard AHU Performance Assessment Rules (APAR) implementation. In addition, a tool (CAL4AHU) was systematically developed which allows the automated calibration of the computational models that represent these psychrometric processes. Finally, the computational models and the automated calibration approach are assessed using a recognised validation methodology based on statistical tests. The case study included two model-based FDD methods using qualitative and quantitative approaches. Experiments and the necessary data collection designed to test the CAL4AHU are described for the AHU under study. The proposed calibration methodology and the associated computational models performed excellently, detecting and isolating a set of faults common in the operation of industrial grade AHUs.
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