Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms

Abstract

Fault detection and diagnosis (FDD) systems enable high cost savings and energy savings that could have economic and environmental impact. This study aims to develop and validate a data-driven FDD system for a chiller. The system uses historical operation data to capture quantitative correlations among system variables. This study evaluated the effectiveness and robustness of eight FDD classification methods based on the experimental data of the chiller (the ASHRAE 1043-RP project). The training data used for the FDD system is classified into four cases. Moreover, true and false positive rates are used to characterize the performance of the classification methods. The results show that local fault is not significantly sensitive to training data, and shows high classification accuracy for all cases. The system fault has a significant effect on the amount of data and the severity levels on the classification accuracy.