Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach
| Autor: | Pascal Yim, Yassine Bouabdallaoui, Belkacem Bennadji, Zoubeir Lafhaj, Laure Ducoulombier |
|---|---|
| Přispěvatelé: | Laboratoire de Mécanique, Multiphysique, Multiéchelle - UMR 9013 (LaMcube), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Bouygues Construction - Travaux Publics, Bouygues Construction |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
IoT
Computer science 020209 energy 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre lcsh:Chemical technology Biochemistry HVAC Predictive maintenance Article Analytical Chemistry [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] predictive maintenance autoencoders 11. Sustainability 021105 building & construction 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation ComputingMilieux_MISCELLANEOUS Building automation [SPI.GCIV.CD]Engineering Sciences [physics]/Civil Engineering/Construction durable business.industry buildings Atomic and Molecular Physics and Optics machine learning Work (electrical) data Information and Communications Technology Artificial intelligence business computer |
| Zdroj: | Sensors Sensors, 2021, 21 (4), pp.1044. ⟨10.3390/s21041044⟩ Sensors, MDPI, 2021, 21 (4), pp.1044. ⟨10.3390/s21041044⟩ Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 1044, p 1044 (2021) Volume 21 Issue 4 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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