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

Icksung Kim,Woohyun Kim

doi:10.3390/en14071945

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.

Highlights

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems are widely used in commercial buildings
This study finds the accuracy of detecting all kinds of faults and severity levels, which are the results of machine learning using limited data
A data-driven fault detection and diagnosis (FDD) method was presented in this study

Summary

Introduction

Ventilation, air conditioning, and refrigeration (HVAC&R) systems are widely used in commercial buildings. They consume a large amount of energy, which forms a major part of the total energy used in commercial buildings. Chillers operating under faulty conditions consume extra energy (up to 30% for commercial buildings) and incur a high cost, provide less comfort control, and generate bad indoor/outdoor air quality [3]. This can be solved by applying fault detection and diagnosis (FDD) systems, so that important faults can be detected and addressed promptly. The FDD method provides an effective means for ensuring efficient and reliable operation of HVAC&R systems [4]

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Conclusion