{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T21:59:54Z","timestamp":1783979994619,"version":"3.55.0"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The M\u00e9tropole Europ\u00e9enne de Lille (MEL)","award":["-"],"award-info":[{"award-number":["-"]}]},{"name":"Bouygues Construction","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21041044","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"1044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":170,"title":["Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5789-395X","authenticated-orcid":false,"given":"Yassine","family":"Bouabdallaoui","sequence":"first","affiliation":[{"name":"Laboratoire de M\u00e9canique Multiphysique Multi\u00e9chelle, LaMcube, UMR 9013, Centrale Lille, CNRS, Universit\u00e9 de Lille, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoubeir","family":"Lafhaj","sequence":"additional","affiliation":[{"name":"Laboratoire de M\u00e9canique Multiphysique Multi\u00e9chelle, LaMcube, UMR 9013, Centrale Lille, CNRS, Universit\u00e9 de Lille, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Yim","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Informatique Signal et Automatique de Lille, CRIStAL, UMR 9189, Centrale Lille, CNRS, Universit\u00e9 de Lille, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8573-4908","authenticated-orcid":false,"given":"Laure","family":"Ducoulombier","sequence":"additional","affiliation":[{"name":"Bouygues Construction, 78280 Guyancourt, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belkacem","family":"Bennadji","sequence":"additional","affiliation":[{"name":"Bouygues Energies et Services, 78280 Guyancourt, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.apenergy.2015.06.043","article-title":"A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building\u2019s dynamic energy performance: Focused on the operation and maintenance phase","volume":"155","author":"Hong","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40327-018-0064-7","article-title":"Machine learning for estimation of building energy consumption and performance: A review","volume":"6","author":"Seyedzadeh","year":"2018","journal-title":"Vis. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s12273-018-0458-4","article-title":"A critical review of fault modeling of HVAC systems in buildings","volume":"11","author":"Li","year":"2018","journal-title":"Build. Simul."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1108\/F-07-2018-0084","article-title":"BIM for FM: Developing information requirements to support facilities management systems","volume":"38","author":"Matarneh","year":"2019","journal-title":"Facilities"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1108\/F-01-2018-0005","article-title":"Improvement of the inspection-repair process with building information modelling and image classification","volume":"37","author":"Zhan","year":"2019","journal-title":"Facilties"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1057\/jba.2008.46","article-title":"Overview of maintenance strategy, acceptable maintenance standard and resources from a building maintenance operation perspective","volume":"4","author":"Lee","year":"2009","journal-title":"J. Build. Apprais."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1108\/13552510210448540","article-title":"Maintenance practices in Hong Kong and the use of the intelligent scheduler","volume":"8","author":"Peter","year":"2002","journal-title":"J. Qual. Maint. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1108\/02632779710160612","article-title":"Data requirements for the prioritization of predictive building maintenance","volume":"15","author":"Pitt","year":"1997","journal-title":"Facilties"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1080\/09613218.2018.1459004","article-title":"Text-mining building maintenance work orders for component fault frequency","volume":"47","author":"Gunay","year":"2018","journal-title":"Build. Res. Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compind.2018.04.015","article-title":"The industrial internet of things (IIoT): An analysis framework","volume":"101","author":"Boyes","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.procir.2016.07.038","article-title":"Industrial Big Data as a Result of IoT Adoption in Manufacturing","volume":"55","author":"Mourtzis","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40887-019-0029-5","article-title":"The quality management ecosystem for predictive maintenance in the Industry 4.0 era","volume":"5","author":"Lee","year":"2019","journal-title":"Int. J. Qual. Innov."},{"key":"ref_14","unstructured":"BSI Standards Publication (2017). Maintenance Terminology, BSI Standards Publication. BS EN 13306:2017."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00170-016-8983-8","article-title":"Cloud-enhanced predictive maintenance","volume":"99","author":"Schmidt","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"ACachada, A., Barbosa, J., Leit\u00f1o, P., Gcraldcs, C.A.S., Deusdado, L., Costa, J., Teixeira, C., Teixeira, J., Moreira, A.H.J., and Moreira, P.M. (2018, January 4\u20137). Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. Proceedings of the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy.","DOI":"10.1109\/ETFA.2018.8502489"},{"key":"ref_17","first-page":"100","article-title":"Maintenance scheduling using data mining techniques and time series models","volume":"13","author":"Gholami","year":"2017","journal-title":"Int. J. Manag. Sci. Eng. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/j.buildenv.2004.01.029","article-title":"Development of an optimal preventive maintenance model based on the reliability assessment for air-conditioning facilities in office buildings","volume":"39","author":"Kwak","year":"2004","journal-title":"Build. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/qre.962","article-title":"A graphical approach for confidence limits of optimal preventive maintenance cycles","volume":"25","author":"Halim","year":"2009","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_20","first-page":"77","article-title":"Mechanical Vibration Analysis of HVAC system and Its Optimization Techniques","volume":"2","author":"Sandeepan","year":"2015","journal-title":"Adv. Res. Electr. Electron. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ning, M., Zaheeruddin, M., and Chen, Z. (2006, January 3\u20136). Fuzzy-Set Based HVAC System Uncertainty Analysis. Proceedings of the NAFIPS 2006\u20142006 Annual Meeting of the North American Fuzzy Information Processing Society, Montr\u00e9al, QC, Canada.","DOI":"10.1109\/NAFIPS.2006.365413"},{"key":"ref_22","unstructured":"Wang, L., and Hong, T. (2014, January 25\u201327). Modeling and Simulation of HVAC Faulty Operation and Performance Degradation due to Maintenance Issues. Proceedings of the ASIM 2012\u20141st Asia conference of International Building Performance Simulation Association, Hong Kong, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mattera, C., Quevedo, J., Escobet, T., Shaker, H.R., and Jradi, M. (2018). A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors. Sensors, 18.","DOI":"10.3390\/s18113931"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1108\/13552519510083156","article-title":"Statistical-based or condition-based preventive maintenance?","volume":"1","author":"Saxena","year":"1995","journal-title":"J. Qual. Maint. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00170-009-2482-0","article-title":"Current status of machine prognostics in condition-based maintenance: A review","volume":"50","author":"Peng","year":"2010","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_26","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_27","unstructured":"Otter, D.W., Medina, J.R., and Kalita, J.K. (2020). A Survey of the Usages of Deep Learning for Natural Language Processing. IEEE Trans. Neural Netw. Learn. Syst., 1\u201321."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","article-title":"Deepr: A Convolutional Net for Medical Records","volume":"21","author":"Nguyen","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_31","first-page":"130","article-title":"A Predictive Preference Model for Maintenance of a Heating Ventilating and Air Conditioning System","volume":"48","author":"Tehrani","year":"2015","journal-title":"IFAC Pap."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.camwa.2010.03.065","article-title":"Predicting remaining useful life of rotating machinery based artificial neural network","volume":"60","author":"Mahamad","year":"2010","journal-title":"Comput. Math. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Kim, J.-M., and Kim, C.H. (2020). Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems. Sensors, 20.","DOI":"10.3390\/s20236886"},{"key":"ref_34","unstructured":"(2016). Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press."},{"key":"ref_35","unstructured":"Gasparin, A., Lukovic, S., and Alippi, C. (2019). Deep Learning for Time Series Forecasting: The Electric Load Case. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1002\/rob.20276","article-title":"Learning long-range vision for autonomous off-road driving","volume":"26","author":"Hadsell","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.apenergy.2017.12.005","article-title":"Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data","volume":"211","author":"Fan","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_38","first-page":"15","article-title":"Analysis of Parallel Process in HVAC Systems Using Deep Autoencoders","volume":"744","author":"Alonso","year":"2017","journal-title":"Progr. Ing. Nat."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, C., Ding, Z., Zhao, D., Yi, J., and Zhang, G. (2017). Building Energy Consumption Prediction: An Extreme Deep Learning Approach. Energies, 10.","DOI":"10.3390\/en10101525"},{"key":"ref_40","first-page":"37","article-title":"Autoencoders, Unsupervised Learning, and Deep Architectures","volume":"27","author":"Baldi","year":"2012","journal-title":"JMLR Workshop Conf. Proc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neucom.2015.08.104","article-title":"Auto-encoder based dimensionality reduction","volume":"184","author":"Wang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Murphree, J. (2016). Machine learning anomaly detection in large systems. IEEE Autotestcon, 1\u20139.","DOI":"10.1109\/AUTEST.2016.7589589"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.enbuild.2017.02.058","article-title":"An ensemble learning framework for anomaly detection in building energy consumption","volume":"144","author":"Araya","year":"2017","journal-title":"Energy Build."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., and Bennadji, B. (2020). Natural Language Processing Model for Managing Maintenance Requests in Buildings. Buildings, 10.","DOI":"10.3390\/buildings10090160"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., and Pei, D. (2019, January 4\u20138). Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330672"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.autcon.2014.12.006","article-title":"A framework for knowledge discovery in massive building automation data and its application in building diagnostics","volume":"50","author":"Fan","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s00500-015-1679-4","article-title":"Big data: The key to energy efficiency in smart buildings","volume":"20","author":"Moreno","year":"2015","journal-title":"Soft Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/JIOT.2017.2647881","article-title":"IoT Considerations, Requirements, and Architectures for Smart Buildings\u2014Energy Optimization and Next-Generation Building Management Systems","volume":"4","author":"Minoli","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.rser.2018.04.013","article-title":"Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency","volume":"90","author":"Schmidt","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Carli, R., Cavone, G., Ben Othman, S., and Dotoli, M. (2020). IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings. Sensors, 20.","DOI":"10.3390\/s20030781"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Akkaya, K., Guvenc, I., Aygun, R., Pala, N., and Kadri, A. (2015, January 9\u201312). IoT-based occupancy monitoring techniques for energy-efficient smart buildings. Proceedings of the 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), New Orleans, LA, USA.","DOI":"10.1109\/WCNCW.2015.7122529"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bourdeau, M., Zhai, X., Nefzaoui, E., Guo, X., and Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustain. Cities Soc., 48.","DOI":"10.1016\/j.scs.2019.101533"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.autcon.2018.10.020","article-title":"Data analytics to improve building performance: A critical review","volume":"97","author":"Gunay","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Marmo, R., Nicolella, M., Polverino, F., Tibaut, A., and Marmo, R. (2019). A Methodology for a Performance Information Model to Support Facility Management. Sustainability, 11.","DOI":"10.3390\/su11247007"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3390\/buildings5010100","article-title":"Building Information Modelling for Smart Built Environments","volume":"5","author":"Zhang","year":"2015","journal-title":"Buildings"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.autcon.2017.10.017","article-title":"Activity theory-based analysis of BIM implementation in building O&M and first response","volume":"85","author":"Lu","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.enbuild.2017.03.032","article-title":"BIM application to building energy performance visualisation and management: Challenges and potential","volume":"144","author":"Gerrish","year":"2017","journal-title":"Energy Build."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.autcon.2014.04.007","article-title":"A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics","volume":"44","author":"Dong","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.autcon.2018.03.007","article-title":"BIM-based framework for automatic scheduling of facility maintenance work orders","volume":"91","author":"Chen","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.autcon.2014.03.012","article-title":"Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management","volume":"43","author":"Motamedi","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E.B., and Turaga, D. Learning Feature Engineering for Classification. Proceedings of the Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19\u201325 August 2017.","DOI":"10.24963\/ijcai.2017\/352"},{"key":"ref_62","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning, O\u2019Reilly Media, Inc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-55320-6","article-title":"Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems","volume":"9","author":"Sagheer","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.icte.2017.12.005","article-title":"A comparative study of LPWAN technologies for large-scale IoT deployment","volume":"5","author":"Mekki","year":"2019","journal-title":"ICT Express"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1080\/23744731.2020.1795514","article-title":"The ASHRAE Great Energy Predictor III competition: Overview and results","volume":"26","author":"Miller","year":"2020","journal-title":"Sci. Technol. Built Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1044\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:39Z","timestamp":1760159979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,3]]},"references-count":66,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041044"],"URL":"https:\/\/doi.org\/10.3390\/s21041044","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,3]]}}}