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Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach
The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significan...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913483/ https://www.ncbi.nlm.nih.gov/pubmed/33546418 http://dx.doi.org/10.3390/s21041044 |
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author | Bouabdallaoui, Yassine Lafhaj, Zoubeir Yim, Pascal Ducoulombier, Laure Bennadji, Belkacem |
author_facet | Bouabdallaoui, Yassine Lafhaj, Zoubeir Yim, Pascal Ducoulombier, Laure Bennadji, Belkacem |
author_sort | Bouabdallaoui, Yassine |
collection | PubMed |
description | The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings. |
format | Online Article Text |
id | pubmed-7913483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79134832021-02-28 Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach Bouabdallaoui, Yassine Lafhaj, Zoubeir Yim, Pascal Ducoulombier, Laure Bennadji, Belkacem Sensors (Basel) Article The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings. MDPI 2021-02-03 /pmc/articles/PMC7913483/ /pubmed/33546418 http://dx.doi.org/10.3390/s21041044 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bouabdallaoui, Yassine Lafhaj, Zoubeir Yim, Pascal Ducoulombier, Laure Bennadji, Belkacem Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title | Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title_full | Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title_fullStr | Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title_full_unstemmed | Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title_short | Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach |
title_sort | predictive maintenance in building facilities: a machine learning-based approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913483/ https://www.ncbi.nlm.nih.gov/pubmed/33546418 http://dx.doi.org/10.3390/s21041044 |
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