Fault Detection and Diagnosis in AHU System with Data Driven Approaches

Abstract

Energy consumption in buildings has become a real concern for scientists and seeking to reduce this consumption is essential. Heating, ventilation, and air conditioning (HVAC) systems account for more than 50% of this consumption. One of the solutions to reduce this excessive consumption is to detect and diagnose faults that can appear instantaneously and quickly with fault diagnostic detection systems (FDD) based on artificial intelligence. The paper presents a strategy based on a data-driven approach for the detection and diagnosis of sensor faults that may appear in the Air Handling Unit (AHU) systems. A Decision Tree, Random Forest and SVM algorithm were used to detect and diagnose temperature sensor faults occurring in the AHU. The comparison between these methods shows that the Random Forest gives the best result with 96% accuracy.