{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:16:30Z","timestamp":1783113390286,"version":"3.54.6"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Z202304392531"],"award-info":[{"award-number":["Z202304392531"]}],"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":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104730","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T09:11:05Z","timestamp":1777626665000},"page":"104730","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PC","title":["A few-shot unknown fault diagnosis framework for heating, ventilation, and air conditioning systems with entropy-based uncertainty guidance"],"prefix":"10.1016","volume":"74","author":[{"given":"Changfu","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104730_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.127972","article-title":"Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data","volume":"278","author":"Fan","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104730_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101674","article-title":"Deep learning forecasting for electric demand applications of cooling systems in buildings","volume":"53","author":"Runge","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104730_b0015","doi-asserted-by":"crossref","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":"10.1016\/j.aei.2026.104730_b0020","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":"Mokhtari","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104730_b0025","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. Inform."},{"key":"10.1016\/j.aei.2026.104730_b0030","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.2026.104730_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.115621","article-title":"Active multi-mode data analysis to improve fault diagnosis in AHUs","volume":"337","author":"Lin","year":"2025","journal-title":"Energy Build."},{"key":"10.1016\/j.aei.2026.104730_b0040","article-title":"advanced engineering informatics - philosophical and methodological foundations with examples from civil and construction engineering","volume":"4","author":"Hartmann","year":"2020","journal-title":"Dev. Built Environ."},{"key":"10.1016\/j.aei.2026.104730_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2025.115817","article-title":"Systematic review on uncertainty quantification in machine learning-based building energy modeling","volume":"218","author":"Xu","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.aei.2026.104730_b0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijrefrig.2020.06.009","article-title":"Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach","volume":"118","author":"Guo","year":"2020","journal-title":"Int. J. Refrig."},{"key":"10.1016\/j.aei.2026.104730_b0055","article-title":"Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers","volume":"51","author":"Yao","year":"2022","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.aei.2026.104730_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2022.103708","article-title":"Fault detection and diagnosis for chiller based on feature-recognition model and Kernel Discriminant Analysis","volume":"79","author":"Bai","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"10.1016\/j.aei.2026.104730_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121591","article-title":"How to improve the application potential of deep learning model in HVAC fault diagnosis: based on pruning and interpretable deep learning method","volume":"348","author":"Gao","year":"2023","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104730_b0070","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s12273-024-1125-6","article-title":"An interpretable graph convolutional neural network based fault diagnosis method for building energy systems","volume":"17","author":"Li","year":"2024","journal-title":"Build Simul.-China"},{"key":"10.1016\/j.aei.2026.104730_b0075","article-title":"Interpretable chiller fault diagnosis based on physics-guided neural networks","volume":"86","author":"Pan","year":"2024","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.aei.2026.104730_b0080","article-title":"The class labels and spatial information based fault diagnosis of air handling unit via combining kernel Fischer discriminant analysis with an improved graph convolutional neural network","volume":"257","author":"Zhang","year":"2026","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104730_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.115659","article-title":"Unsupervised domain adaptation for HVAC fault diagnosis using contrastive adaptation network","volume":"337","author":"Ghalamsiah","year":"2025","journal-title":"Energy Build."},{"key":"10.1016\/j.aei.2026.104730_b0090","doi-asserted-by":"crossref","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":"10.1016\/j.aei.2026.104730_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110233","article-title":"Non-parametric semi-supervised chiller fault diagnosis via variational compressor under severe few labeled samples","volume":"146","author":"Han","year":"2025","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104730_b0100","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1007\/s12273-023-1041-1","article-title":"Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts","volume":"16","author":"Fan","year":"2023","journal-title":"Build Simul.-China"},{"key":"10.1016\/j.aei.2026.104730_b0105","doi-asserted-by":"crossref","first-page":"170","DOI":"10.26599\/BDMA.2022.9020015","article-title":"Semi-supervised machine learning for fault detection and diagnosis of a rooftop unit","volume":"6","author":"Albayati","year":"2023","journal-title":"Big Data Min. Anal."},{"key":"10.1016\/j.aei.2026.104730_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2020.110318","article-title":"Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods","volume":"225","author":"Ebrahimifakhar","year":"2020","journal-title":"Energy Build."},{"key":"10.1016\/j.aei.2026.104730_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105540","article-title":"Augmented data driven self-attention deep learning method for imbalanced fault diagnosis of the HVAC chiller","volume":"117","author":"Shen","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104730_b0120","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s12273-023-1086-1","article-title":"Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network","volume":"17","author":"Wu","year":"2024","journal-title":"Build. Simul.-China"},{"key":"10.1016\/j.aei.2026.104730_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.applthermaleng.2023.122051","article-title":"Fault detection and diagnosis of energy system based on deep learning image recognition model under the condition of imbalanced samples","volume":"238","author":"Ruan","year":"2024","journal-title":"Appl. Therm. Eng."},{"key":"10.1016\/j.aei.2026.104730_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.115531","article-title":"Hybrid-CGAN: a Hybrid approach combining simulation and generative adversarial networks for fault detection and diagnosis in buildings","volume":"335","author":"Han","year":"2025","journal-title":"Energy Build."},{"key":"10.1016\/j.aei.2026.104730_b0135","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.2026.104730_b0140","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":"Energy Build."},{"key":"10.1016\/j.aei.2026.104730_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122545","article-title":"Intelligent fault diagnosis for air handing units based on improved generative adversarial network and deep reinforcement learning","volume":"240","author":"Yan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104730_b0150","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1109\/TASE.2018.2876611","article-title":"Identifying unseen faults for smart buildings by incorporating expert knowledge with data","volume":"16","author":"Li","year":"2019","journal-title":"IEEE T. Autom. Sci. Eng."},{"key":"10.1016\/j.aei.2026.104730_b0155","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12273-021-0791-x","article-title":"A real-time abnormal operation pattern detection method for building energy systems based on association rule bases","volume":"15","author":"Zhang","year":"2022","journal-title":"Build. Simul.-China"},{"key":"10.1016\/j.aei.2026.104730_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.127056","article-title":"A few-shot learning framework for HVAC fault diagnosis in data centers with minimal data required","volume":"402","author":"Yan","year":"2026","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104730_b0165","series-title":"Proceedings of the 31st International Conference on Advance Neural Information Process System","first-page":"4078","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017"},{"key":"10.1016\/j.aei.2026.104730_b0170","series-title":"Proceedings of the 37th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research","first-page":"1597","article-title":"Framework for Contrastive Learning of Visual Representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.aei.2026.104730_b0175","first-page":"510","article-title":"Selective Kernel networks","volume":"2019","author":"Li","year":"2019","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"10.1016\/j.aei.2026.104730_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113806","article-title":"Class-relevant feature density estimator for open set fault classification of industrial equipment using vibration signals","volume":"224","author":"Mei","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104730_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104133","article-title":"A novel dimensional variational prototypical network for industrial few-shot fault diagnosis with unseen faults","volume":"162","author":"Peng","year":"2024","journal-title":"Comput. Ind."},{"key":"10.1016\/j.aei.2026.104730_b0190","first-page":"7507","article-title":"Glocal energy-based learning for few-shot open-set recognition","volume":"2023","author":"Wang","year":"2023","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"10.1016\/j.aei.2026.104730_b0195","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Y. Yao, X. Liu, L. Qin, W. Wang, W. Deng, Open-set facial expression recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, 38 (2024) 646\u2013654.","DOI":"10.1609\/aaai.v38i1.27821"},{"key":"10.1016\/j.aei.2026.104730_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128214","article-title":"Few-shot open-set recognition via pairwise discriminant aggregation","volume":"602","author":"Jin","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104730_b0205","first-page":"1","article-title":"An open-set classification method with small samples for rotating machinery","volume":"73","author":"Han","year":"2024","journal-title":"IEEE T. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104730_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129276","article-title":"TNPNet: an approach to Few-shot open-set recognition via contextual transductive learning","volume":"621","author":"Wu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104730_b0215","doi-asserted-by":"crossref","first-page":"13147","DOI":"10.1007\/s11071-024-09733-2","article-title":"A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network","volume":"112","author":"He","year":"2024","journal-title":"Nonlinear Dynam."},{"key":"10.1016\/j.aei.2026.104730_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104077","article-title":"A hybrid cross-domain few-shot bearing fault diagnosis method combining multi-scale feature association and physical information","volume":"69","author":"He","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104730_b0225","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.aei.2026.104730_b0230","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41597-020-0398-6","article-title":"Building fault detection data to aid diagnostic algorithm creation and performance testing","volume":"7","author":"Granderson","year":"2020","journal-title":"Sci. Data"},{"key":"10.1016\/j.aei.2026.104730_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.2026.104730_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114678","article-title":"Enhanced bearing fault diagnosis under strong noise: a deep residual network with combined attention mechanisms","volume":"330","author":"Liu","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104730_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110331","article-title":"A Transformer-based self-supervised learning model for fault diagnosis of air-conditioning systems with limited labeled data","volume":"146","author":"Hua","year":"2025","journal-title":"Eng. Appl. Artif. Intel."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004222?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004222?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:22:21Z","timestamp":1783110141000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626004222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":49,"alternative-id":["S1474034626004222"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104730","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A few-shot unknown fault diagnosis framework for heating, ventilation, and air conditioning systems with entropy-based uncertainty guidance","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104730","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":"104730"}}