A data-driven method for Fault Detection and Diagnosis in Air Handling Units
Keywords: Fault Detection, Fault Diagnosis, Data-Driven Methods, Artificial Neural Networks, Random Forests, Air Handling Units
AbstractAir Handling Units (AHUs) are essential refrigeration equipment to provide thermal comfort in buildings and commercial establishments. However, the maintenance strategies adopted for AHUs have low efficiency. In addition to the higher energy consumption, an AHU with a high degradation level has a higher probability of presenting a failure event. Many establishments already have both the automation and the sensors needed to benefit from the data generated by the air handling systems. A fault detection and diagnosis (FDD) method can benefit from these data in order to identify inefficiencies in AHUs, allowing the implementation of an efficient maintenance strategy. This paper presents a data-driven method that uses Artificial Neural Networks with the Random Forest algorithm to identify the operation condition of AHUs, to alert the operator in case of a failure event. The results observed in numerical experiments show an accuracy of 99.3% and a computed F1-score of 0.983. The model was tested with out-of-sample data and the results kept satisfactory.