{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:44:40Z","timestamp":1777873480863,"version":"3.51.4"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.eswa.2026.132641","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T14:44:17Z","timestamp":1777214657000},"page":"132641","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Zero-shot transfer learning bearing fault diagnosis based on generative adversarial network with multi-attribute vector fusion"],"prefix":"10.1016","volume":"324","author":[{"given":"Bin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengke","family":"Jiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8399-5781","authenticated-orcid":false,"given":"Lijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132641_b0005","unstructured":"Arjovsky, M., S. Chintala, L. Bottou, Wasserstein generative adversarial networks, International conference on machine learning, PMLR, 2017, pp. 214-223."},{"key":"10.1016\/j.eswa.2026.132641_b0010","doi-asserted-by":"crossref","first-page":"9633","DOI":"10.1109\/TII.2024.3383459","article-title":"A multiattribute learning model for zero-sample mechanical fault diagnosis","volume":"20","author":"Cai","year":"2024","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0015","unstructured":"Chen, Z., Z. Zhao, J. Guo, J. Li, Z. Huang, SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning, arXiv preprint arXiv:2503.10252, (2025)."},{"key":"10.1016\/j.eswa.2026.132641_b0020","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"330","article-title":"Transzero: Attribute-guided transformer for zero-shot learning","author":"Chen","year":"2022"},{"key":"10.1016\/j.eswa.2026.132641_b0025","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TNNLS.2022.3155602","article-title":"GNDAN: Graph navigated dual attention network for zero-shot learning","volume":"35","author":"Chen","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.132641_b0030","doi-asserted-by":"crossref","first-page":"8562","DOI":"10.1109\/TII.2025.3584512","article-title":"Zero-shot fault diagnosis in industrial processes using graph-regularized coupled dictionary learning","volume":"21","author":"Deng","year":"2025","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0035","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TII.2020.2988208","article-title":"Fault description based attribute transfer for zero-sample industrial fault diagnosis","volume":"17","author":"Feng","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0040","series-title":"DeViSE: a deep visual-semantic embedding model","first-page":"2121","author":"Frome","year":"2013"},{"key":"10.1016\/j.eswa.2026.132641_b0045","unstructured":"S. Fujieda, K. Takayama, T. Hachisuka, Wavelet convolutional neural networks, arXiv preprint arXiv:1805.08620, (2018)."},{"key":"10.1016\/j.eswa.2026.132641_b0050","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132641_b0055","article-title":"Improved training of wasserstein gans","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132641_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111524","article-title":"A novel incremental method for bearing fault diagnosis that continuously incorporates unknown fault types","volume":"216","author":"He","year":"2024","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.132641_b0065","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"23627","article-title":"Visual-augmented dynamic semantic prototype for generative zero-shot learning","author":"Hou","year":"2024"},{"key":"10.1016\/j.eswa.2026.132641_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106507","article-title":"Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved transformer","volume":"124","author":"Hou","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108970","article-title":"Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network","volume":"136","author":"Hu","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0080","doi-asserted-by":"crossref","first-page":"7022","DOI":"10.1109\/TII.2022.3210215","article-title":"Semantic-consistent embedding for zero-shot fault diagnosis","volume":"19","author":"Hu","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117761","article-title":"An improved dual-channel CNN-BILSTM fusion attention model for fault diagnosis of aero-engine bearings","volume":"253","author":"Huang","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.eswa.2026.132641_b0090","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","article-title":"Attribute-based classification for zero-shot visual object categorization","volume":"36","author":"Lampert","year":"2014","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0095","unstructured":"A.B.L. Larsen, S.K. S\u00f8nderby, H. Larochelle, O. Winther, Autoencoding beyond pixels using a learned similarity metric, International conference on machine learning, PMLR, 2016, pp. 1558-1566."},{"key":"10.1016\/j.eswa.2026.132641_b0100","doi-asserted-by":"crossref","DOI":"10.36001\/phme.2016.v3i1.1577","article-title":"Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification","author":"Lessmeier","year":"2016","journal-title":"PHM society European conference"},{"key":"10.1016\/j.eswa.2026.132641_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.130101","article-title":"Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants","volume":"290","author":"Li","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132641_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.112025","article-title":"Noise-robust multi-view graph neural network for fault diagnosis of rotating machinery","volume":"224","author":"Li","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.132641_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103319","article-title":"Zero-shot fault diagnosis using soft semantic embedding of diffusion-encoded probability","volume":"65","author":"Li","year":"2025","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0120","first-page":"1","article-title":"Fault transfer diagnosis of hydraulic pump via dynamic simulation and an improved domain adversarial neural network","volume":"74","author":"Liang","year":"2025","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.132641_b0125","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/TII.2025.3526478","article-title":"A novel zero-shot learning method with feature generation for intelligent fault diagnosis","volume":"21","author":"Liao","year":"2025","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120696","article-title":"Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals","volume":"230","author":"Lin","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0135","doi-asserted-by":"crossref","first-page":"10394","DOI":"10.1109\/TNNLS.2025.3528885","article-title":"Concept-aware graph convolutional network for compositional zero-shot learning","volume":"36","author":"Liu","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.132641_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106577","article-title":"Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis","volume":"95","author":"Lv","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.132641_b0145","unstructured":"Sundararajan, M., A. Taly, Q. Yan, Axiomatic Attribution for Deep Networks, in: P. Doina, T. Yee Whye (Eds.) Proceedings of the 34th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 2017, pp. 3319--3328."},{"key":"10.1016\/j.eswa.2026.132641_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109704","article-title":"Broad zero-shot diagnosis for rotating machinery with untrained compound faults","volume":"242","author":"Ma","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132641_b0155","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2794","article-title":"Least squares generative adversarial networks","author":"Mao","year":"2017"},{"key":"10.1016\/j.eswa.2026.132641_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127948","article-title":"A zero-fault sample diesel engine early warning and localisation method based on multi-cylinder similarity and memory augmentation","volume":"284","author":"Mo","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0165","series-title":"Visual Attributes","first-page":"11","article-title":"An embarrassingly simple approach to zero-shot learning","author":"Romera-Paredes","year":"2017"},{"key":"10.1016\/j.eswa.2026.132641_b0170","first-page":"8239","article-title":"Generalized zero- and few-shot learning via aligned variational autoencoders","volume":"2019","author":"Sch\u00f6nfeld","year":"2019","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.1016\/j.eswa.2026.132641_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.132389","article-title":"Zero-shot compound fault diagnosis with semantic graph embedding and multi-stage fusion","volume":"668","author":"Shen","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132641_b0180","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64","author":"Smith","year":"2015","journal-title":"Mechanical systems and signal processing"},{"key":"10.1016\/j.eswa.2026.132641_b0185","article-title":"Learning structured output representation using deep conditional generative models","volume":"28","author":"Sohn","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132641_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114130","article-title":"Extending latent semantic analysis to manage its syntactic blindness","volume":"165","author":"Suleman","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121852","article-title":"Text classification with improved word embedding and adaptive segmentation","volume":"238","author":"Sun","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0200","first-page":"1","article-title":"An open set diagnosis method for rolling bearing faults based on prototype and reconstructed integrated network","volume":"72","author":"Sun","year":"2023","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.132641_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111568","article-title":"Interpretable and robust fault diagnosis of rotating machinery in noisy environments via improved high-order spatial interactions network","volume":"158","author":"Wang","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117113","article-title":"Enhancing robustness of cross-machine fault diagnosis via an improved domain adversarial neural network and self-adversarial training","volume":"250","author":"Wang","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.eswa.2026.132641_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126452","article-title":"Few-shot fault diagnosis of axial piston pump based on prior knowledge-embedded meta learning vision transformer under variable operating conditions","volume":"269","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0220","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TII.2025.3641795","article-title":"MCSANet: Cross-modal semantic alignment in multi-attribute learning for zero-shot bearing fault diagnosis","volume":"22","author":"Wu","year":"2026","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132641_b0225","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","article-title":"Zero-shot learning\u2014A comprehensive evaluation of the good, the bad and the ugly","volume":"41","author":"Xian","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0230","first-page":"9376","article-title":"Attentive region embedding network for zero-shot learning","volume":"2019","author":"Xie","year":"2019","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.1016\/j.eswa.2026.132641_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116197","article-title":"Zero-shot learning for compound fault diagnosis of bearings","volume":"190","author":"Xu","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120875","article-title":"A label information vector generative zero-shot model for the diagnosis of compound faults","volume":"233","author":"Xu","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132641_b0245","doi-asserted-by":"crossref","first-page":"35589","DOI":"10.1109\/JIOT.2025.3580594","article-title":"Semi-supervised class adaptive prototype network for cross-working rolling bearing fault diagnosis under limited samples","volume":"12","author":"Yao","year":"2025","journal-title":"IEEE Internet of Things Journal"},{"key":"10.1016\/j.eswa.2026.132641_b0250","doi-asserted-by":"crossref","first-page":"16041","DOI":"10.1007\/s10489-022-04342-1","article-title":"An effective zero-shot learning approach for intelligent fault detection using 1D CNN","volume":"53","author":"Zhang","year":"2023","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.eswa.2026.132641_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109964","article-title":"Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study","volume":"245","author":"Zhao","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132641_b0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111430","article-title":"Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method","volume":"215","author":"Zhong","year":"2024","journal-title":"Mechanical Systems and Signal Processing"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601554X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601554X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:57:25Z","timestamp":1777571845000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095741742601554X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":52,"alternative-id":["S095741742601554X"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132641","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Zero-shot transfer learning bearing fault diagnosis based on generative adversarial network with multi-attribute vector fusion","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132641","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":"132641"}}