Actuator Fault Diagnosis in Multi-Zone HVAC Systems using 2D Convolutional Neural Networks

This paper presents a novel supervised on-line fault diagnosis strategy in Heating, Ventilation, and Air conditioning (HVAC) systems for actuator faults using 2D Convolutional Neural Networks. It is based on an efficient 1-Dimensional to 2-Dimensional data transformation that eliminates the need for advanced signals pre-processing. The proposed approach aims to address the limitations found in the previous works in terms of the diagnosis accuracy by adopting the recently evolving topology of the Convolutional Neural Networks. It is developed and validated using simulation data collected for a 3-zone HVAC system simulator using Transient System Simulation Tool (TRNSYS). The proposed approach demonstrates an improved performance when compared to the other data-driven approaches for actuator fault diagnosis in HVAC systems.