Fault detection and diagnosis for variable refrigerant flow systems by using virtual sensors and deep learning

Abstract

The primary objective of this work was to implement and evaluate a fault detection and diagnostic system for variable refrigerant flow (VRF) systems with multiple indoor units. To achieve this goal, virtual sensors and deep learning models are developed to predict the system performance whose direct measurement could be expensive or difficult. The primary challenges in implementing a diagnostic and performance monitoring system are the high initial costs of additional physical sensors. However, the combination of virtual sensor and the deep learning model can identify and isolate a condenser fouling fault. The virtual sensors were used to determine the condenser air flow rate in and the performance of a VRF system equipped with a variable-speed compressor and fans. An important step in automated fault detection and diagnostics is evaluating whether a condenser fouling fault is adequately significant to warrant service based on various criteria, including the potential to degrade comfort, increase in energy usage that can affect the overall system performance operation, and operating conditions that can shorten VRF system life. The deep learning model is developed to determine the performance degradation ratio between the performance estimated using the outputs of virtual sensors and that estimated using deep learning-based expected performance models. The fault impact models can help determine whether a fault is sufficiently serious to warrant a service recommendation.