@inproceedings{613b16c4f2704cfb9ffe2b86e06863f3,
title = "Explainable AI based Fault Detection and Diagnosis System for Air Handling Units",
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.",
keywords = "Buildings, Explainable Artificial Intelligence, Fault Detection and Diagnosis, HVAC, Machine Learning",
author = "Juri Belikov and Molika Meas and Ram Machlev and Ahmet Kose and Aleksei Tepljakov and Lauri Loo and Eduard Petlenkov and Yoash Levron",
note = "Publisher Copyright: {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 19th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2022 ; Conference date: 14-07-2022 Through 16-07-2022",
year = "2022",
doi = "https://doi.org/10.5220/0011350000003271",
language = "الإنجليزيّة",
isbn = "9789897585852",
series = "Proceedings of the International Conference on Informatics in Control, Automation and Robotics",
pages = "271--279",
editor = "Giuseppina Gini and Henk Nijmeijer and Wolfram Burgard and Filev, {Dimitar P.}",
booktitle = "ICINCO 2022 - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics",
}