{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T03:09:02Z","timestamp":1786417742989,"version":"3.56.0"},"reference-count":121,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund (ERDF)","award":["KK.01.2.1.02.0303"],"award-info":[{"award-number":["KK.01.2.1.02.0303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings\u2019 energy is mostly used for heating and\/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.<\/jats:p>","DOI":"10.3390\/s23010001","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T02:55:47Z","timestamp":1671504947000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8722-3965","authenticated-orcid":false,"given":"Iva","family":"Mateti\u0107","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4758-7972","authenticated-orcid":false,"given":"Ivan","family":"\u0160tajduhar","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-9549","authenticated-orcid":false,"given":"Igor","family":"Wolf","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-8369","authenticated-orcid":false,"given":"Sandi","family":"Ljubic","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.enbuild.2007.03.007","article-title":"A review on buildings energy consumption information","volume":"40","author":"Ortiz","year":"2008","journal-title":"Energy Build."},{"key":"ref_2","unstructured":"Delmastro, C., and Martinez-Gordon, R. (2022, September 10). Cooling. Available online: https:\/\/www.iea.org\/reports\/space-cooling."},{"key":"ref_3","unstructured":"Goodson, T. (2022, September 10). Heating. Available online: https:\/\/www.iea.org\/reports\/heating."},{"key":"ref_4","unstructured":"The International Energy Agency (IEA) (2022, September 10). Net Zero by 2050\u2014A Roadmap for the Global Energy Sector. Available online: https:\/\/www.iea.org\/reports\/net-zero-by-2050."},{"key":"ref_5","unstructured":"Lecamwasam, L., Wilson, J., and Chokolich, D. (2012). Guide to Best Practice Maintenance & Operation of HVAC Systems for Energy Efficiency."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/ACCESS.2020.3040980","article-title":"Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"986","DOI":"10.3390\/civileng2040053","article-title":"Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC","volume":"2","year":"2021","journal-title":"CivilEng"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116601","DOI":"10.1016\/j.apenergy.2021.116601","article-title":"Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives","volume":"287","author":"Himeur","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110492","DOI":"10.1016\/j.enbuild.2020.110492","article-title":"Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review","volume":"229","author":"Mirnaghi","year":"2020","journal-title":"Energy Build."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101429","DOI":"10.1016\/j.jobe.2020.101429","article-title":"Overview and implementation of dynamic thermoeconomic & diagnosis analyses in HVAC&R systems","volume":"32","author":"Lazzaretto","year":"2020","journal-title":"J. Build. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.enbuild.2014.06.042","article-title":"A review of fault detection and diagnosis methodologies on air-handling units","volume":"82","author":"Yu","year":"2014","journal-title":"Energy Build."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Park, Y.J., Fan, S.K.S., and Hsu, C.Y. (2020). A review on fault detection and process diagnostics in industrial processes. Processes, 8.","DOI":"10.3390\/pr8091123"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101692","DOI":"10.1016\/j.jobe.2020.101692","article-title":"A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis","volume":"33","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112395","DOI":"10.1016\/j.rser.2022.112395","article-title":"A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems","volume":"161","author":"Chen","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhang, T., Li, H., Woradechjumroen, D., and Liu, X. (2014). HVAC Equipment, Unitary: Fault Detection and Diagnosis. Encyclopedia of Energy Engineering and Technology, CRC Press. [2nd ed.].","DOI":"10.1081\/E-EEE2-120051345"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1016\/j.compind.2014.06.003","article-title":"Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application","volume":"65","author":"Alzghoul","year":"2014","journal-title":"Comput. Ind."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10789669.2005.10391123","article-title":"Review article: Methods for fault detection, diagnostics, and prognostics for building systems\u2014A review, part I","volume":"11","author":"Katipamula","year":"2005","journal-title":"HVAC R Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.enbenv.2019.11.003","article-title":"A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis","volume":"1","author":"Zhao","year":"2020","journal-title":"Energy Built Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.autcon.2009.11.019","article-title":"Overview of HVAC system simulation","volume":"19","author":"Hensen","year":"2010","journal-title":"Autom. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","article-title":"A review on the prediction of building energy consumption","volume":"16","author":"Zhao","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Melgaard, S.P., Andersen, K.H., Marszal-Pomianowska, A., Jensen, R.L., and Heiselberg, P.K. (2022). Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review. Energies, 15.","DOI":"10.3390\/en15124366"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alghanmi, A., Yunusa-Kaltungo, A., and Edwards, R. (2021, January 23\u201327). A comparative study of faults detection techniques on HVAC systems. Proceedings of the 2021 IEEE PES\/IAS PowerAfrica, Nairobi, Kenya.","DOI":"10.1109\/PowerAfrica52236.2021.9543158"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110232","DOI":"10.1016\/j.enbuild.2020.110232","article-title":"A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system","volume":"224","author":"Zhou","year":"2020","journal-title":"Energy Build."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"26","DOI":"10.15377\/2409-5818.2019.06.3","article-title":"A Review of Fault Detection and Diagnosis Methodologies for Air-Handling Units","volume":"6","author":"Guarino","year":"2019","journal-title":"Glob. J. Energy Technol. Res. Updat."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rosato, A., Guarino, F., Filomena, V., Sibilio, S., and Maffei, L. (2020). Experimental calibration and validation of a simulation model for fault detection of HVAC systems and application to a case study. Energies, 13.","DOI":"10.3390\/en13153948"},{"key":"ref_26","unstructured":"Shi, Y., and Augenbroe, G. (2019, January 2\u20134). High Resolution Model-based HVAC Fault Detection and Diagnosis (FDD) Considering Building Operation Uncertainty. Proceedings of the Building Simulation Conference Proceedings, Rome, Italy."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104100","DOI":"10.1016\/j.conengprac.2019.07.018","article-title":"Identification of a control-oriented energy model for a system of fan coil units","volume":"91","year":"2019","journal-title":"Control Eng. Pract."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Joe, J., Im, P., and Dong, J. (2020). Empirical modeling of direct expansion (Dx) cooling system for multiple research use cases. Sustainability, 12.","DOI":"10.3390\/su12208738"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101023","DOI":"10.1016\/j.jobe.2019.101023","article-title":"Bilinear model-based diagnosis of lock-in-place failures of variable-air-volume HVAC systems of multizone buildings","volume":"28","author":"A","year":"2020","journal-title":"J. Build. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101788","DOI":"10.1016\/j.csite.2022.101788","article-title":"Investigating thermostat sensor offset impacts on operating performance and thermal comfort of three different HVAC systems in Wuhan, China","volume":"31","author":"Li","year":"2022","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1177\/0143624418775881","article-title":"A decentralized sensor fault detection and self-repair method for HVAC systems","volume":"39","author":"Wang","year":"2018","journal-title":"Build. Serv. Eng. Res. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110781","DOI":"10.1016\/j.enbuild.2021.110781","article-title":"Versatile AHU fault detection\u2014Design, field validation and practical application","volume":"237","author":"Nehasil","year":"2021","journal-title":"Energy Build."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103781","DOI":"10.1016\/j.autcon.2021.103781","article-title":"Formalized control logic fault definition with ontological reasoning for air handling units","volume":"129","author":"Lei","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, G., Pritoni, M., Chen, Y., and Granderson, J. (2020). Development and implementation of fault-correction algorithms in fault detection and diagnostics tools. Energies, 13.","DOI":"10.3390\/en13102598"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lin, G., Crowe, E., and Granderson, J. (2021). Development of a unified taxonomy for hvac system faults. Energies, 14.","DOI":"10.3390\/en14175581"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1080\/17512549.2018.1545143","article-title":"Case study results: Fault detection in air-handling units in buildings","volume":"14","author":"Deshmukh","year":"2020","journal-title":"Adv. Build. Energy Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Woradechjumroen, D., and Leephakpreeda, T. (2021). Fault detection involving unfavorable interaction effects to enhance the fault diagnostics of refrigeration systems in commercial supermarkets. J. Braz. Soc. Mech. Sci. Eng., 43.","DOI":"10.1007\/s40430-021-02980-z"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hietaharju, P., Ruusunen, M., and Leivisk\u00e4, K. (2018). A dynamic model for indoor temperature prediction in buildings. Energies, 11.","DOI":"10.3390\/en11061477"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/23744731.2017.1318008","article-title":"A review of fault detection and diagnostics methods for building systems","volume":"24","author":"Kim","year":"2018","journal-title":"Sci. Technol. Built Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.enbuild.2017.07.053","article-title":"HVAC system energy optimization using an adaptive hybrid metaheuristic","volume":"152","author":"Ghahramani","year":"2017","journal-title":"Energy Build."},{"key":"ref_41","unstructured":"Adetola, V., Bengea, S., Kang, K., Kelman, A., Leonardi, F., Li, P., Lovett, T., Sarkar, S., and Vichik, S. (2014, January 23\u201325). Model predictive control and fault detection and diagnostics of a building heating, ventilation, and air conditioning system. Proceedings of the 2014 International High Performance Buildings Conference (IHPBC), Berkeley, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., and Bennadji, B. (2021). Predictive maintenance in building facilities: A machine learning-based approach. Sensors, 21.","DOI":"10.3390\/s21041044"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102874","DOI":"10.1016\/j.scs.2021.102874","article-title":"A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data","volume":"70","author":"Fan","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Simmini, F., Rampazzo, M., Peterle, F., Susto, G.A., and Beghi, A. (2021). A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems. IEEE Trans. Control. Syst. Technol.","DOI":"10.1109\/TCST.2021.3107200"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111255","DOI":"10.1016\/j.enbuild.2021.111255","article-title":"A framework for a multi-source, data-driven building energy management toolkit","volume":"250","author":"Markus","year":"2021","journal-title":"Energy Build."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1080\/23744731.2021.1877966","article-title":"Fault detection and diagnosis for the screw chillers using multi-region XGBoost model","volume":"27","author":"Zhang","year":"2021","journal-title":"Sci. Technol. Built Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"110795","DOI":"10.1016\/j.enbuild.2021.110795","article-title":"Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks","volume":"236","author":"Cheng","year":"2021","journal-title":"Energy Build."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Burgas, L., Colomer, J., Melendez, J., Gamero, F.I., and Herraiz, S. (2021). Integrated unfold-pca monitoring application for smart buildings: An ahu application example. Energies, 14.","DOI":"10.3390\/en14010235"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"110733","DOI":"10.1016\/j.enbuild.2021.110733","article-title":"Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units","volume":"234","author":"Fan","year":"2021","journal-title":"Energy Build."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103014","DOI":"10.1016\/j.jobe.2021.103014","article-title":"A novel temporal convolutional network via enhancing feature extraction for the chiller fault diagnosis","volume":"42","author":"Li","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"107957","DOI":"10.1016\/j.buildenv.2021.107957","article-title":"Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency","volume":"200","author":"Zhu","year":"2021","journal-title":"Build. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111467","DOI":"10.1016\/j.enbuild.2021.111467","article-title":"Fault detection and diagnosis of the air handling unit via an enhanced kernel slow feature analysis approach considering the time-wise and batch-wise dynamics","volume":"253","author":"Zhang","year":"2021","journal-title":"Energy Build."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102111","DOI":"10.1016\/j.jobe.2020.102111","article-title":"A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states","volume":"35","author":"Yun","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111044","DOI":"10.1016\/j.enbuild.2021.111044","article-title":"A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network","volume":"246","author":"Li","year":"2021","journal-title":"Energy Build."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TASE.2020.2998586","article-title":"Machine Learning-Based Prognostics for Central Heating and Cooling Plant Equipment Health Monitoring","volume":"18","author":"Yang","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"70502","DOI":"10.1109\/ACCESS.2021.3078550","article-title":"Autonomic Management of a Building\u2019s Multi-HVAC System Start-Up","volume":"9","author":"Aguilar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111275","DOI":"10.1016\/j.enbuild.2021.111275","article-title":"Fault detection diagnostic for HVAC systems via deep learning algorithms","volume":"250","author":"Taheri","year":"2021","journal-title":"Energy Build."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111069","DOI":"10.1016\/j.enbuild.2021.111069","article-title":"A hybrid data-driven simultaneous fault diagnosis model for air handling units","volume":"245","author":"Wu","year":"2021","journal-title":"Energy Build."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tun, W., Wong, J.K.W., and Ling, S.H. (2021). Hybrid random forest and support vector machine modeling for hvac fault detection and diagnosis. Sensors, 21.","DOI":"10.3390\/s21248163"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"108057","DOI":"10.1016\/j.buildenv.2021.108057","article-title":"An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems","volume":"203","author":"Li","year":"2021","journal-title":"Build. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s12273-020-0650-1","article-title":"A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings","volume":"14","author":"Piscitelli","year":"2021","journal-title":"Build. Simul."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ijrefrig.2020.08.014","article-title":"A hybrid deep forest approach for outlier detection and fault diagnosis of variable refrigerant flow system","volume":"120","author":"Zeng","year":"2020","journal-title":"Int. J. Refrig."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"106698","DOI":"10.1016\/j.buildenv.2020.106698","article-title":"Generative adversarial network for fault detection diagnosis of chillers","volume":"172","author":"Yan","year":"2020","journal-title":"Build. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"110351","DOI":"10.1016\/j.enbuild.2020.110351","article-title":"Ensemble learning with member optimization for fault diagnosis of a building energy system","volume":"226","author":"Han","year":"2020","journal-title":"Energy Build."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"110445","DOI":"10.1016\/j.enbuild.2020.110445","article-title":"Cluster analysis-based anomaly detection in building automation systems","volume":"228","author":"Gunay","year":"2020","journal-title":"Energy Build."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Martinez-Viol, V., Urbano, E.M., Kampouropoulos, K., Delgado-Prieto, M., and Romeral, L. (2020, January 8\u201311). Support vector machine based novelty detection and FDD framework applied to building AHU systems. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.","DOI":"10.1109\/ETFA46521.2020.9212088"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"109689","DOI":"10.1016\/j.enbuild.2019.109689","article-title":"Unsupervised learning for fault detection and diagnosis of air handling units","volume":"210","author":"Yan","year":"2020","journal-title":"Energy Build."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"22001","DOI":"10.1051\/e3sconf\/202017222001","article-title":"Identifying faults in the building system based on model prediction and residuum analysis","volume":"172","author":"Parzinger","year":"2020","journal-title":"E3S Web Conf. Edp Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/TASE.2020.2990566","article-title":"A Metadata inference method for building automation systems with limited semantic information","volume":"17","author":"Chen","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"401","DOI":"10.3390\/smartcities3020021","article-title":"A case study based approach for remote fault detection using multi-level machine learning in a smart building","volume":"3","author":"Dey","year":"2020","journal-title":"Smart Cities"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"109885","DOI":"10.1016\/j.rser.2020.109885","article-title":"A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics","volume":"127","author":"Li","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_72","unstructured":"Miyata, S., Akashi, Y., Lim, J., Kuwahara, Y., and Tanaka, K. (2019, January 2\u20134). Model-based fault detection and diagnosis for HVAC systems using convolutional neural network. Proceedings of the Building Simulation Conference Proceedings, International Building Performance Simulation Association, Rome, Italy."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhong, C., Yan, K., Dai, Y., Jin, N., and Lou, B. (2019). Energy efficiency solutions for buildings: Automated fault diagnosis of air handling units using generative adversarial networks. Energies, 12.","DOI":"10.3390\/en12030527"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TASE.2019.2948101","article-title":"Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems","volume":"17","author":"Li","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Tabassam, N., Amin, S., and Obermaisser, R. (2019). Fault detection and diagnosis in hvac systems using diagnostic multi-query graphs. Proceedings of the Proceedings\u20142019 IEEE Intl Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA\/BDCloud\/SustainCom\/SocialCom, Xiamen, China, 16\u201318 December 2019, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00095"},{"key":"ref_76","first-page":"872","article-title":"Semi-supervised learning techniques for automated fault detection and diagnosis of HVAC systems","volume":"Volume 2018","author":"Dey","year":"2018","journal-title":"Proceedings of the Proceedings\u2014International Conference on Tools with Artificial Intelligence, ICTAI, Volos, Greece, 5\u20137 November 2018"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.future.2018.02.019","article-title":"Smart building creation in large scale HVAC environments through automated fault detection and diagnosis","volume":"108","author":"Dey","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_78","first-page":"78","article-title":"HVAC fault detection","volume":"54","author":"Wiggins","year":"2012","journal-title":"ASHRAE J."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"G\u00e1lvez, A., Diez-Olivan, A., Seneviratne, D., and Galar, D. (2021). Fault detection and RUL estimation for railway HVAC systems using a hybrid model-based approach. Sustainability, 13.","DOI":"10.3390\/su13126828"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"G\u00e1lvez, A., Seneviratne, D., and Galar, D. (2021). Hybrid Model Development for HVAC System in Transportation. Technologies, 9.","DOI":"10.3390\/technologies9010018"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Elnour, M., and Meskin, N. (2021). Novel Actuator Fault Diagnosis Framework for Multizone HVAC Systems Using 2-D Convolutional Neural Networks. IEEE Trans. Autom. Sci. Eng.","DOI":"10.1109\/ICIoT48696.2020.9089508"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Rosato, A., Guarino, F., Sibilio, S., Entchev, E., Masullo, M., and Maffei, L. (2021). Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment. Energies, 14.","DOI":"10.3390\/en14175362"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Liao, H., Cai, W., Cheng, F., Dubey, S., and Rajesh, P.B. (2021). An online data-driven fault diagnosis method for air handling units by rule and convolutional neural networks. Sensors, 21.","DOI":"10.3390\/s21134358"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"111293","DOI":"10.1016\/j.enbuild.2021.111293","article-title":"A novel fault diagnosis and self-calibration method for air-handling units using Bayesian Inference and virtual sensing","volume":"250","author":"Liu","year":"2021","journal-title":"Energy Build."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"106632","DOI":"10.1016\/j.buildenv.2019.106632","article-title":"Fault detection and diagnosis for indoor air quality in DCV systems: Application of 4S3F method and effects of DBN probabilities","volume":"174","author":"Taal","year":"2020","journal-title":"Build. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Parzinger, M., Hanfstaengl, L., Sigg, F., Spindler, U., Wellisch, U., and Wirnsberger, M. (2020). Residual analysis of predictive modelling data for automated fault detection in building\u2019s heating, ventilation and air conditioning systems. Sustainability, 12.","DOI":"10.3390\/su12176758"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"100955","DOI":"10.1016\/j.jobe.2019.100955","article-title":"A computationally efficient method for fault diagnosis of fan-coil unit terminals in building Heating Ventilation and Air Conditioning systems","volume":"27","author":"Ranade","year":"2020","journal-title":"J. Build. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"110476","DOI":"10.1016\/j.enbuild.2020.110476","article-title":"Bayesian method for HVAC plant sensor fault detection and diagnosis","volume":"228","author":"Ng","year":"2020","journal-title":"Energy Build."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Dowling, C.P., and Zhang, B. (2020, January 1\u20133). Transfer learning for HVAC system fault detection. Proceedings of the 2020 American Control Conference (ACC), Denver, CO, USA.","DOI":"10.23919\/ACC45564.2020.9147772"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Gharsellaoui, S., Mansouri, M., Refaat, S.S., Abu-Rub, H., and Messaoud, H. (2020). Multivariate features extraction and effective decision making using machine learning approaches. Energies, 13.","DOI":"10.3390\/en13030609"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"110368","DOI":"10.1016\/j.enbuild.2020.110368","article-title":"Development, implementation, and evaluation of a fault detection and diagnostics system based on integrated virtual sensors and fault impact models","volume":"228","author":"Kim","year":"2020","journal-title":"Energy Build."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.enbuild.2018.12.032","article-title":"Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold","volume":"185","author":"Chakraborty","year":"2019","journal-title":"Energy Build."},{"key":"ref_94","first-page":"893","article-title":"A diagnostic Bayesian network method to diagnose building energy performance","volume":"Volume 2","author":"Taal","year":"2019","journal-title":"Proceedings of the Building Simulation Conference Proceedings, Rome, Italy, 2\u20134 September 2019"},{"key":"ref_95","first-page":"101092","article-title":"An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining","volume":"44","author":"Xu","year":"2021","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"111426","DOI":"10.1016\/j.enbuild.2021.111426","article-title":"Analytic hierarchy process-based fuzzy post mining method for operation anomaly detection of building energy systems","volume":"252","author":"Zhang","year":"2021","journal-title":"Energy Build."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"113492","DOI":"10.1016\/j.apenergy.2019.113492","article-title":"An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems","volume":"253","author":"Zhang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"110369","DOI":"10.1016\/j.enbuild.2020.110369","article-title":"Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules","volume":"226","author":"Piscitelli","year":"2020","journal-title":"Energy Build."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ciani, L., Guidi, G., Patrizi, G., and Galar, D. (2021). Condition-based maintenance of hvac on a high-speed train for fault detection. Electronics, 10.","DOI":"10.3390\/electronics10121418"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Novikova, E., and Bestuzhev, M. (2020, January 8\u201311). Exploration of the Anomalies in HVAC Data Using Image Similarity Assessment. Proceedings of the 2020 9th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro.","DOI":"10.1109\/MECO49872.2020.9134218"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"107022","DOI":"10.1016\/j.compchemeng.2020.107022","article-title":"A hybrid modeling approach integrating first-principles knowledge with statistical methods for fault detection in HVAC systems","volume":"142","author":"Hassanpour","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"171892","DOI":"10.1109\/ACCESS.2020.3019365","article-title":"Interval-valued features based machine learning technique for fault detection and diagnosis of uncertain HVAC systems","volume":"8","author":"Gharsellaoui","year":"2020","journal-title":"IEEE Access"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Zhu, H., Yang, W., Li, S., and Pang, A. (2022). An Effective Fault Detection Method for HVAC Systems Using the LSTM-SVDD Algorithm. Buildings, 12.","DOI":"10.3390\/buildings12020246"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"110691","DOI":"10.1016\/j.enbuild.2020.110691","article-title":"Automated fault detection of residential air-conditioning systems using thermostat drive cycles","volume":"236","author":"Chintala","year":"2021","journal-title":"Energy Build."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/JAS.2020.1003123","article-title":"Scalable distributed sensor fault diagnosis for smart buildings","volume":"7","author":"Papadopoulos","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1080\/23744731.2020.1785812","article-title":"Gray-box virtual sensor of the supply air temperature of air handling units","volume":"26","author":"Ahamed","year":"2020","journal-title":"Sci. Technol. Built Environ."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., and Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. IPMI 2017: Information Processing in Medical Imaging, Springer.","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref_108","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_109","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"100306","DOI":"10.1016\/j.cosrev.2020.100306","article-title":"A critical overview of outlier detection methods","volume":"38","author":"Smiti","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/ICJECE.2020.3018433","article-title":"Evaluation of Dimensionality Reduction Techniques for Load Profiling Application in Smart Grid Environment","volume":"44","author":"Aleshinloye","year":"2021","journal-title":"IEEE Can. J. Electr. Comput. Eng."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models, CRC Press.","DOI":"10.1201\/9781315108230"},{"key":"ref_113","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O\u2019Reilly Media, Inc."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s10462-019-09682-y","article-title":"A review of unsupervised feature selection methods","volume":"53","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_115","unstructured":"G\u00e9ron, A. (2022). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O\u2019Reilly Media, Inc."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Chamasemani, F.F., and Singh, Y.P. (2011, January 27\u201329). Multi-class Support Vector Machine (SVM) Classifiers\u2014An Application in Hypothyroid Detection and Classification. Proceedings of the 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications, Penang, Malaysia.","DOI":"10.1109\/BIC-TA.2011.51"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Heo, J., Heo, J., Payne, W.V., Domanski, P.A., and Du, Z. (2015). Self-Training of a Fault-Free Model for Residential Air Conditioner Fault Detection and Diagnostics.","DOI":"10.6028\/NIST.TN.1881"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1007\/s11227-017-2228-y","article-title":"An innovative neural network approach for stock market prediction","volume":"76","author":"Pang","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_119","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_121","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/1\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:44:25Z","timestamp":1760147065000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,20]]},"references-count":121,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010001"],"URL":"https:\/\/doi.org\/10.3390\/s23010001","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,20]]}}}