Using a hybrid method to construct a computational efficient cooling coil model for an automated single-duct variable air volume system fault detection and diagnosis

This paper introduces a hybrid method that integrates physical laws, system knowledge and real-time measured data in order to develop a computational efficient cooling coil model that can detect air-handling unit (AHU) system operation faults and inefficiencies. A thermal balance model to describe the cooling energy use of an AHU is built based on the first law in order to understand equipment mechanisms and to determine the variables that impact cooling coil energy performance. In order to avoid humidity measurements of supply air and return air that are not readily available in AHU operations, the energy balance model is then simplified into a lumped temperature-based model for dry coil operations and a lumped enthalpy-based model for wet coil operations. Parameters in the lumped models are determined using the least squares method along with short periods of measured data. Through experiments, the proposed cooling load baseline produced ±5.5% errors at 95% confidence on an AHU with a cooling capacity of 28 kW (8 t). The effectiveness of fault detection using the studied cooling model is also tested by introducing a stuck outdoor air damper fault on the test AHU.