乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T11:10:13Z","timestamp":1727521813513},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007538","name":"Civil Aviation Administration of China","doi-asserted-by":"publisher","award":["U1733108"],"id":[{"id":"10.13039\/501100007538","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1733108"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin Municipality","doi-asserted-by":"publisher","award":["21JCZDJC00770"],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.engappai.2024.109020","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T17:40:03Z","timestamp":1721583603000},"page":"109020","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions"],"prefix":"10.1016","volume":"136","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7310-0921","authenticated-orcid":false,"given":"Zhiwu","family":"Shang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0009-0009-8721-8320","authenticated-orcid":false,"given":"Lutai","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0032-0891","authenticated-orcid":false,"given":"Cailu","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Hongchuan","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2024.109020_bib1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2019.04.010","article-title":"Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method","volume":"352","author":"An","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2024.109020_bib2","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1109\/TITS.2020.3029946","article-title":"Data-driven fault diagnosis for traction systems in high-speed trains: a survey, challenges, and perspectives","volume":"23","author":"Chen","year":"2022","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10.1016\/j.engappai.2024.109020_bib3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/TR.2022.3177930","article-title":"Deep attention relation network: a zero-shot learning method for bearing fault diagnosis under unknown domains","volume":"72","author":"Chen","year":"2023","journal-title":"IEEE Trans. Reliab."},{"key":"10.1016\/j.engappai.2024.109020_bib4","first-page":"1462","article-title":"Hybrid attribute-based zero-shot image classification","volume":"45","author":"Cheng","year":"2017","journal-title":"Acta Electron. Sin."},{"key":"10.1016\/j.engappai.2024.109020_bib5","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TPAMI.2018.2867870","article-title":"Generative zero-shot learning via low-rank embedded semantic dictionary","volume":"41","author":"Ding","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib6","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 Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2024.109020_bib7","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1007\/s10845-019-01485-w","article-title":"A zero-shot learning method for fault diagnosis under unknown working loads","volume":"31","author":"Gao","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.engappai.2024.109020_bib8","doi-asserted-by":"crossref","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","article-title":"Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data","volume":"66","author":"Guo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2024.109020_bib9","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","article-title":"Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application","volume":"97","author":"Han","year":"2020","journal-title":"ISA Trans."},{"key":"10.1016\/j.engappai.2024.109020_bib10","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TIP.2019.2947780","article-title":"Deep unbiased embedding transfer for zero-shot learning","volume":"29","author":"Jia","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2024.109020_bib11","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neunet.2022.05.024","article-title":"MGLNN: semi-supervised learning via multiple graph cooperative learning neural networks","volume":"153","author":"Jiang","year":"2022","journal-title":"Neural Network."},{"key":"10.1016\/j.engappai.2024.109020_bib12","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 Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: a review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108487","article-title":"A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: theories, applications and challenges","volume":"167","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib15","doi-asserted-by":"crossref","first-page":"6785","DOI":"10.1109\/TIE.2019.2935987","article-title":"Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places","volume":"67","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2024.109020_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105269","article-title":"Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment","volume":"115","author":"Liang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib17","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/acd5f3","article-title":"Bearing failure diagnosis at time-varying speed based on adaptive clustered fractional Gabor transform","volume":"34","author":"Liu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2024.109020_bib18","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.sigpro.2016.07.028","article-title":"Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification","volume":"130","author":"Lu","year":"2017","journal-title":"Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib19","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":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2024.109020_bib20","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.measurement.2018.08.010","article-title":"An enhancement denoising autoencoder for rolling bearing fault diagnosis","volume":"130","author":"Meng","year":"2018","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2024.109020_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110845","article-title":"Optimization of rolling bearing dynamic model based on improved golden jackal optimization algorithm and sensitive feature fusion","volume":"204","author":"Pan","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib22","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compind.2015.03.001","article-title":"An overview on fault diagnosis and nature-inspired optimal control of industrial process applications","volume":"74","author":"Precup","year":"2015","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2024.109020_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102007","article-title":"DenseSPH-YOLOv5: an automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism","volume":"56","author":"Roy","year":"2023","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.engappai.2024.109020_bib24","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1007\/s00521-021-06651-x","article-title":"A fast accurate fine-grain object detection model based on YOLOv4 deep neural network","volume":"34","author":"Roy","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.engappai.2024.109020_bib25","doi-asserted-by":"crossref","first-page":"8972","DOI":"10.3390\/app12188972","article-title":"Deep residual learning for image recognition: a survey","volume":"12","author":"Shafiq","year":"2022","journal-title":"Appl. Sci.-Basel"},{"key":"10.1016\/j.engappai.2024.109020_bib26","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\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3324926","article-title":"A survey of zero-shot learning: settings, methods, and applications","volume":"10","author":"Wang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.engappai.2024.109020_bib28","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TSMC.2017.2754287","article-title":"A new deep transfer learning based on sparse auto-encoder for fault diagnosis","volume":"49","author":"Wen","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"10.1016\/j.engappai.2024.109020_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108202","article-title":"Few-shot transfer learning for intelligent fault diagnosis of machine","volume":"166","author":"Wu","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2024.109020_bib30","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 Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib31","series-title":"IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)","first-page":"264","article-title":"Zero-shot audio classification based on class label embeddings","author":"Xie","year":"2019"},{"key":"10.1016\/j.engappai.2024.109020_bib32","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 Syst. Appl."},{"key":"10.1016\/j.engappai.2024.109020_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119642","article-title":"A zero-shot fault semantics learning model for compound fault diagnosis","volume":"221","author":"Xu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2024.109020_bib34","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 Syst. Appl."},{"key":"10.1016\/j.engappai.2024.109020_bib35","doi-asserted-by":"crossref","first-page":"9733","DOI":"10.1109\/TPAMI.2021.3127346","article-title":"ZeroNAS: differentiable generative adversarial networks search for zero-shot learning","volume":"44","author":"Yan","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib36","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2024.109020_bib37","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s40430-022-03965-2","article-title":"A zero-shot learning fault diagnosis method of rolling bearing based on extended semantic information under unknown conditions","volume":"45","author":"Yang","year":"2022","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"10.1016\/j.engappai.2024.109020_bib38","doi-asserted-by":"crossref","first-page":"3755","DOI":"10.1109\/TCYB.2018.2850750","article-title":"Zero-shot learning via latent space encoding","volume":"49","author":"Yu","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2024.109020_bib39","doi-asserted-by":"crossref","first-page":"2908","DOI":"10.1109\/TCYB.2017.2751741","article-title":"Transductive zero-shot learning with a self-training dictionary approach","volume":"48","author":"Yu","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2024.109020_bib40","doi-asserted-by":"crossref","first-page":"110895","DOI":"10.1109\/ACCESS.2019.2934233","article-title":"Limited data rolling bearing fault diagnosis with few-shot learning","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2024.109020_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107370","article-title":"Deep transductive network for generalized zero shot learning","volume":"105","author":"Zhang","year":"2020","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.engappai.2024.109020_bib42","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1109\/TIM.2006.876386","article-title":"Detecting the blockage of the sensing lines of a differential-pressure flow sensor in a dynamic process using wavelet transform techniques","volume":"55","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2024.109020_bib43","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":"Appl. Intell."},{"key":"10.1016\/j.engappai.2024.109020_bib44","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.eswa.2018.05.032","article-title":"A new subset based deep feature learning method for intelligent fault diagnosis of bearing","volume":"110","author":"Zhang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2024.109020_bib45","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep residual shrinkage networks for fault diagnosis","volume":"16","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Ind. Inf."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197624011783?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197624011783?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T10:30:43Z","timestamp":1727519443000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197624011783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":45,"alternative-id":["S0952197624011783"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2024.109020","relation":{},"ISSN":["0952-1976"],"issn-type":[{"type":"print","value":"0952-1976"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2024.109020","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109020"}}