{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:15:07Z","timestamp":1783167307988,"version":"3.54.6"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008860","name":"Wuhan University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008860","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51906181"],"award-info":[{"award-number":["51906181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104916","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:21:28Z","timestamp":1781068888000},"page":"104916","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["A CFlow-based data generation framework for chiller fault diagnosis with a latent space evaluation method"],"prefix":"10.1016","volume":"76","author":[{"given":"Guannan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanmin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104916_b0005","series-title":"Net Zero by 2050: a roadmap for the global energy sector","first-page":"2021","author":"International Energy Agency","year":"2021"},{"key":"10.1016\/j.aei.2026.104916_b0010","unstructured":"International Energy Agency, Tracking Clean Energy Progress 2023, Paris, 2023."},{"key":"10.1016\/j.aei.2026.104916_b0015","first-page":"34","article-title":"Energy saving potential, environmental and economic importance of evaporative cooling system: A review","volume":"6","author":"Okafor","year":"2019","journal-title":"European Journal of Advances in Engineering and Technology"},{"key":"10.1016\/j.aei.2026.104916_b0020","first-page":"3","article-title":"Review article: methods for fault detection, diagnostics, and prognostics for building systems\u2014A review, Part I, HVAC&R","volume":"11","author":"Katipamula","year":"2005","journal-title":"Research"},{"key":"10.1016\/j.aei.2026.104916_b0025","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.apenergy.2011.10.037","article-title":"Building operation and energy performance: monitoring, analysis and optimisation toolkit","volume":"101","author":"Costa","year":"2013","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104916_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102810","article-title":"Autoencoder-based fault detection using building automation system data","volume":"62","author":"El Mokhtari","year":"2024","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104016","article-title":"A new open set fault diagnosis method based on adversarial discrimination and deep evidential fusion under limited labeled samples","volume":"69","author":"Han","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103360","article-title":"A hybrid sensor fault detection and diagnosis method for air-handling unit based on multivariate analysis merged with deep learning","volume":"65","author":"Gao","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103469","article-title":"Novel data-to-image method for heating ventilation and air conditioning fault detection and diagnosis in the built world","volume":"66","author":"Bi","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2024.110956","article-title":"A semi-labelled dataset for fault detection in air handling units from a large-scale office","volume":"57","author":"Wang","year":"2024","journal-title":"Data Brief"},{"key":"10.1016\/j.aei.2026.104916_b0055","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1038\/s41597-025-05825-9","article-title":"Real operational labeled data of air handling units from office, auditorium, and hospital buildings","volume":"12","author":"Wang","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.aei.2026.104916_b0060","article-title":"Fault Diagnosis of Air Handling Units in an Auditorium Using Real Operational Labeled Data across Different Operation","volume":"39","author":"Park","year":"2025","journal-title":"Modes"},{"key":"10.1016\/j.aei.2026.104916_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2025.113257","article-title":"Automated fault diagnosis detection of air handling units using real operational labelled data and transformer-based methods at 24-hour operation hospital","volume":"282","author":"Wang","year":"2025","journal-title":"Build. Environ."},{"key":"10.1016\/j.aei.2026.104916_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2023.110885","article-title":"Automated fault detection and diagnosis of chiller water plants based on convolutional neural network and knowledge distillation","volume":"245","author":"Gao","year":"2023","journal-title":"Build. Environ."},{"key":"10.1016\/j.aei.2026.104916_b0075","doi-asserted-by":"crossref","first-page":"41192","DOI":"10.1038\/s41598-025-24959-9","article-title":"Fault-class coverage-aligned combined training for AFDD of AHUs across multiple buildings","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104916_b0080","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aei.2018.04.010","article-title":"A large-scale evaluation of automated metadata inference approaches on sensors from air handling units","volume":"37","author":"Gao","year":"2018","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.117139","article-title":"Problem of data imbalance in building energy load prediction: Concept, influence, and solution","volume":"297","author":"Zhang","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104916_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123854","article-title":"A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: a case study of China electricity statistics","volume":"373","author":"Chen","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104916_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102800","article-title":"State-of-the-art review and synthesis: a requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies","volume":"62","author":"Ma","year":"2024","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0100","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijrefrig.2021.01.009","article-title":"Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments","volume":"125","author":"Zhou","year":"2021","journal-title":"Int. J. Refrig"},{"key":"10.1016\/j.aei.2026.104916_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.applthermaleng.2019.113933","article-title":"Chiller fault diagnosis with field sensors using the technology of imbalanced data","volume":"159","author":"Fan","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"10.1016\/j.aei.2026.104916_b0110","doi-asserted-by":"crossref","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":"10.1016\/j.aei.2026.104916_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2021.107982","article-title":"Chiller fault detection and diagnosis with anomaly detective generative adversarial network","volume":"201","author":"Yan","year":"2021","journal-title":"Build. Environ."},{"key":"10.1016\/j.aei.2026.104916_b0120","first-page":"321","volume":"16","author":"Chawla","year":"2002","journal-title":"SMOTE: synthetic minority over-sampling technique"},{"key":"10.1016\/j.aei.2026.104916_b0125","first-page":"433","volume":"2","author":"Abdi","year":"2010","journal-title":"Principal component analysis"},{"key":"10.1016\/j.aei.2026.104916_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2021.111144","article-title":"Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods","volume":"247","author":"Wang","year":"2021","journal-title":"Energ. Build."},{"key":"10.1016\/j.aei.2026.104916_b0135","first-page":"373","volume":"13","author":"Alkhawaldeh","year":"2023","journal-title":"Challenges and limitations of synthetic minority oversampling techniques in machine learning"},{"key":"10.1016\/j.aei.2026.104916_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.126774","article-title":"A stable, reliable and interpretable diffusion model for HVAC FDD with data unavailability","volume":"401","author":"Yan","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104916_b0145","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Generative adversarial nets"},{"key":"10.1016\/j.aei.2026.104916_b0150","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"10.1016\/j.aei.2026.104916_b0155","series-title":"International Conference on Machine Learning","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.aei.2026.104916_b0160","first-page":"6840","volume":"33","author":"Ho","year":"2020","journal-title":"Denoising diffusion probabilistic models"},{"key":"10.1016\/j.aei.2026.104916_b0165","doi-asserted-by":"crossref","DOI":"10.3390\/en12030527","article-title":"Energy efficiency solutions for buildings: automated fault diagnosis of air handling units using generative adversarial networks","volume":"12","author":"Zhong","year":"2019","journal-title":"Energies"},{"key":"10.1016\/j.aei.2026.104916_b0170","series-title":"International Conference On Machine Learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"},{"key":"10.1016\/j.aei.2026.104916_b0175","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Improved training of wasserstein gans"},{"key":"10.1016\/j.aei.2026.104916_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2025.112529","article-title":"End-to-end residual learning embedded ACWGAN for AHU FDD with limited fault data","volume":"270","author":"Bi","year":"2025","journal-title":"Build. Environ."},{"key":"10.1016\/j.aei.2026.104916_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2023.113072","article-title":"Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system","volume":"289","author":"Du","year":"2023","journal-title":"Energ. Buildings"},{"key":"10.1016\/j.aei.2026.104916_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116459","article-title":"A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data","volume":"285","author":"Li","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104916_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.116447","article-title":"A hybrid SMOTE and Trans-CWGAN for data imbalance in real operational AHU AFDD: a case study of an auditorium building","volume":"348","author":"Wang","year":"2025","journal-title":"Energ. Build."},{"key":"10.1016\/j.aei.2026.104916_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2021.111423","article-title":"Quantitative assessments on advanced data synthesis strategies for enhancing imbalanced AHU fault diagnosis performance","volume":"252","author":"Fan","year":"2021","journal-title":"Energ. Build."},{"key":"10.1016\/j.aei.2026.104916_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2022.112207","article-title":"Integrated generative networks embedded with ensemble classifiers for fault detection and diagnosis under small and imbalanced data of building air condition system","volume":"268","author":"Zhang","year":"2022","journal-title":"Energ. Build."},{"key":"10.1016\/j.aei.2026.104916_b0210","article-title":"Augmentation framework for HVAC fault diagnosis based on denoising diffusion models","volume":"106","author":"Zhang","year":"2025","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.aei.2026.104916_b0215","first-page":"153651","article-title":"Variations in variational autoencoders-a comparative","volume":"8","author":"Wei","year":"2020","journal-title":"evaluation"},{"key":"10.1016\/j.aei.2026.104916_b0220","first-page":"88811","article-title":"Generative adversarial networks (GANs): introduction, taxonomy, variants, limitations","volume":"83","author":"Sharma","year":"2024","journal-title":"and applications"},{"key":"10.1016\/j.aei.2026.104916_b0225","first-page":"1","volume":"56","author":"Yang","year":"2023","journal-title":"Diffusion models: A comprehensive survey of methods and applications"},{"key":"10.1016\/j.aei.2026.104916_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102024","article-title":"Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction","volume":"57","author":"Love","year":"2023","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104916_b0235","series-title":"Development of Analysis Tools For The Evaluation Of Fault Detection and Diagnostics in Chillers","author":"Comstock","year":"1999"},{"key":"10.1016\/j.aei.2026.104916_b0240","unstructured":"L. Dinh, J. Sohl-Dickstein,S. Bengio, Density estimation using real nvp, (2016)."},{"key":"10.1016\/j.aei.2026.104916_b0245","doi-asserted-by":"crossref","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":"10.1016\/j.aei.2026.104916_b0250","first-page":"533","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Learning representations by back-propagating errors"},{"key":"10.1016\/j.aei.2026.104916_b0255","series-title":"Unsupervised Representation Learning with Deep Convolutional Generative","author":"Radford","year":"2015"},{"key":"10.1016\/j.aei.2026.104916_b0260","article-title":"Gans trained by a two time-scale update rule converge to a local nash","volume":"30","author":"Heusel","year":"2017","journal-title":"equilibrium"},{"key":"10.1016\/j.aei.2026.104916_b0265","first-page":"723","volume":"13","author":"Gretton","year":"2012","journal-title":"A kernel two-sample test"},{"key":"10.1016\/j.aei.2026.104916_b0270","first-page":"1798","volume":"35","author":"Bengio","year":"2013","journal-title":"m. intelligence, Representation learning: A review and new perspectives"},{"key":"10.1016\/j.aei.2026.104916_b0275","series-title":"Support-vector networks","first-page":"273","volume":"20","author":"Cortes","year":"1995"},{"key":"10.1016\/j.aei.2026.104916_b0280","first-page":"5","volume":"45","author":"Breiman","year":"2001","journal-title":"Random Forests"},{"key":"10.1016\/j.aei.2026.104916_b0285","first-page":"21","volume":"13","author":"Cover","year":"1967","journal-title":"Nearest neighbor pattern classification"},{"key":"10.1016\/j.aei.2026.104916_b0290","doi-asserted-by":"crossref","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":"Energ. Build."},{"key":"10.1016\/j.aei.2026.104916_b0295","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ijrefrig.2017.11.003","article-title":"Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis","volume":"86","author":"Yan","year":"2018","journal-title":"Int. J. Refrig"},{"key":"10.1016\/j.aei.2026.104916_b0300","series-title":"Noise Reduction in Speech Processing","first-page":"1","article-title":"Pearson Correlation Coefficient","author":"Benesty","year":"2009"},{"key":"10.1016\/j.aei.2026.104916_b0305","series-title":"International Joint Conference On Artificial Intelligence","first-page":"973","article-title":"The foundations of cost-sensitive learning","author":"Elkan","year":"2001"},{"key":"10.1016\/j.aei.2026.104916_b0310","first-page":"427","volume":"45","author":"Sokolova","year":"2009","journal-title":"A systematic analysis of performance measures for classification tasks"},{"key":"10.1016\/j.aei.2026.104916_b0315","series-title":"Fundamentals of Engineering Thermodynamics","author":"Moran","year":"2010"},{"key":"10.1016\/j.aei.2026.104916_b0320","series-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"Glorot","year":"2010"},{"key":"10.1016\/j.aei.2026.104916_b0325","unstructured":"I. Loshchilov, F. Hutter, SGDR: Stochastic gradient descent with warm restarts, (2016)."},{"key":"10.1016\/j.aei.2026.104916_b0330","first-page":"838","volume":"30","author":"Polyak","year":"1992","journal-title":"optimization, Acceleration of stochastic approximation by averaging"},{"key":"10.1016\/j.aei.2026.104916_b0335","series-title":"Data mining: Concepts and techniques","author":"Han","year":"2012"},{"key":"10.1016\/j.aei.2026.104916_b0351","series-title":"A practical guide to support vector classification","author":"Hsu","year":"2003"},{"key":"10.1016\/j.aei.2026.104916_b0345","first-page":"686","volume":"378","author":"Raissi","year":"2019","journal-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations"},{"key":"10.1016\/j.aei.2026.104916_b0350","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"Scarselli","year":"2009","journal-title":"The Graph Neural Network Model"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006087?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006087?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:15:46Z","timestamp":1783163746000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626006087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":70,"alternative-id":["S1474034626006087"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104916","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A CFlow-based data generation framework for chiller fault diagnosis with a latent space evaluation method","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104916","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104916"}}