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

Icksung Kim and Woohyun Kim
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Icksung Kim: Department of Mechanical Engineering, Chonnam National University, Gwangju 61186, Korea
Woohyun Kim: Department of Mechanical Engineering, Chonnam National University, Gwangju 61186, Korea

Energies, 2021, vol. 14, issue 7, 1-24

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.

Keywords: chiller; fault detection and diagnosis (FDD); machine learning; data-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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