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Authors: Juri Belikov 1 ; Molika Meas 1 ; 2 ; Ram Machlev 3 ; Ahmet Kose 4 ; 2 ; Aleksei Tepljakov 4 ; Lauri Loo 2 ; Eduard Petlenkov 4 and Yoash Levron 3

Affiliations: 1 Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia ; 2 R8Technologies O Ü, Lõõtsa 8a, Tallinn 11415, Estonia ; 3 The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel ; 4 Department of Computer Systems, Tallinn University of Technology, 12618 Tallinn, Estonia

Keyword(s): Buildings, HVAC, Fault Detection and Diagnosis, Machine Learning, Explainable Artificial Intelligence.

Abstract: Fault detection and diagnosis (FDD) methods are designed to determine whether the equipment in buildings is functioning under normal or faulty conditions and aim to identify the type or nature of a fault. Recent years have witnessed an increased interest in the application of machine learning algorithms to FDD problems. Nevertheless, a possible problem is that users may find it difficult to understand the prediction process made by a black-box system that lacks interpretability. This work presents a method that explains the outputs of an XGBoost-based classifier using an eXplainable Artificial Intelligence technique. The proposed approach is validated using real data collected from a commercial facility.

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Paper citation in several formats:
Belikov, J.; Meas, M.; Machlev, R.; Kose, A.; Tepljakov, A.; Loo, L.; Petlenkov, E. and Levron, Y. (2022). Explainable AI based Fault Detection and Diagnosis System for Air Handling Units. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-585-2; ISSN 2184-2809, SciTePress, pages 271-279. DOI: 10.5220/0011350000003271

@conference{icinco22,
author={Juri Belikov. and Molika Meas. and Ram Machlev. and Ahmet Kose. and Aleksei Tepljakov. and Lauri Loo. and Eduard Petlenkov. and Yoash Levron.},
title={Explainable AI based Fault Detection and Diagnosis System for Air Handling Units},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2022},
pages={271-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011350000003271},
isbn={978-989-758-585-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Explainable AI based Fault Detection and Diagnosis System for Air Handling Units
SN - 978-989-758-585-2
IS - 2184-2809
AU - Belikov, J.
AU - Meas, M.
AU - Machlev, R.
AU - Kose, A.
AU - Tepljakov, A.
AU - Loo, L.
AU - Petlenkov, E.
AU - Levron, Y.
PY - 2022
SP - 271
EP - 279
DO - 10.5220/0011350000003271
PB - SciTePress

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