{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:06:57Z","timestamp":1774890417474,"version":"3.50.1"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100011789","name":"International Science and Technology Cooperation Project of Jilin Province Science and Technology Department","doi-asserted-by":"publisher","award":["20210402080GH"],"award-info":[{"award-number":["20210402080GH"]}],"id":[{"id":"10.13039\/501100011789","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tim.2022.3178483","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T20:56:48Z","timestamp":1653685008000},"page":"1-12","source":"Crossref","is-referenced-by-count":113,"title":["The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-8060","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6901-0221","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4808-1802","authenticated-orcid":false,"given":"Guowei","family":"Cai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China"}]}],"member":"263","reference":[{"issue":"8","key":"ref1","first-page":"11","article-title":"Fault diagnosis of wind turbines gearbox bearings based on information fusion","volume":"43","author":"Lu","year":"2020","journal-title":"J. Chongqing Univ."},{"issue":"14","key":"ref2","first-page":"129","article-title":"Application of big data processing technology in fault diagnosis and early warning of wind turbine gearbox","volume":"40","author":"Zhang","year":"2016","journal-title":"Automat. Electr. Power Syst."},{"issue":"3","key":"ref3","first-page":"180","article-title":"Research progress on fault diagnosis and state prediction of wind turbine","volume":"45","author":"Li","year":"2021","journal-title":"Automat. Electr. Power Syst."},{"issue":"3","key":"ref4","first-page":"849","article-title":"Review of fault diagnosis methods of large-scale wind turbines","volume":"42","author":"Zeng","year":"2018","journal-title":"Power Syst. Technol."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.09.040"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2017.01.056"},{"issue":"19","key":"ref7","first-page":"124","article-title":"Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network","volume":"37","author":"Li","year":"2018","journal-title":"J. Vib. Shock"},{"issue":"5","key":"ref8","first-page":"56","article-title":"Fault diagnosis of roller bearing based on the Pseudo Wigner-Ville distribution and wavelet transform","volume":"28","author":"Ye","year":"2012","journal-title":"Mach. Des. Res."},{"issue":"7","key":"ref9","first-page":"2011","article-title":"Fault identification of converter used in wind power generation based on wavelet packet analysis","volume":"37","author":"Shen","year":"2013","journal-title":"Power Syst. Technol."},{"issue":"3","key":"ref10","first-page":"37","article-title":"Research on fault diagnosis of gear wear based on Hilbert\u2013Huang transform","volume":"25","author":"Li","year":"2005","journal-title":"J. Vib., Meas. Diagnosis"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.12.010"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s12613-021-2290-6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3082264"},{"issue":"10","key":"ref14","first-page":"27","article-title":"Fault diagnosis based on a deep convolution variational autoencoder network","volume":"39","author":"She","year":"2018","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3025396"},{"issue":"21","key":"ref16","first-page":"7496","article-title":"Fault diagnosis method of wind turbine planetary gearbox based on improved generative adversarial network","volume":"41","author":"Li","year":"2021","journal-title":"Proc. CSEE"},{"issue":"21","key":"ref17","first-page":"2617","article-title":"Rolling bearing fault diagnosis method based on enhanced deep auto-encoder network","volume":"32","author":"Tong","year":"2021","journal-title":"China Mech. Eng."},{"issue":"11","key":"ref18","first-page":"307","article-title":"Optimized stacked denoising auto-encoders (SDAE)-based fault diagnosis of rolling bearing","volume":"42","author":"Yu","year":"2021","journal-title":"Acta Energiae Solaris Sinica"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2017.2669947"},{"issue":"3","key":"ref20","first-page":"1","article-title":"Research overview of variational auto-encoders models","volume":"55","author":"Zhai","year":"2019","journal-title":"Comput. Eng. Appl."},{"issue":"8","key":"ref21","first-page":"3052","article-title":"Multi-classification method of smart meter fault types based on CVAE-CNN model under imbalanced dataset","volume":"45","author":"Gao","year":"2021","journal-title":"Power Syst. Technol."},{"key":"ref22","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-32-396126-4.00015-1"},{"issue":"12","key":"ref24","first-page":"2500","article-title":"Research progress on application of generative adversarial networks in various fields","volume":"46","author":"Liu","year":"2020","journal-title":"Acta Automatica Sinica"},{"issue":"3","key":"ref25","first-page":"74","article-title":"Generative adversarial network: An overview","volume":"40","author":"Luo","year":"2019","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref26","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3119135"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref29","volume-title":"Case Western Reserve University Bearing Data Center Website","year":"2021"},{"issue":"15","key":"ref30","first-page":"4558","article-title":"A two-dimensional convolutional neural network optimization method for bearing fault diagnosis","volume":"39","author":"Xiao","year":"2019","journal-title":"Proc. CSEE"},{"key":"ref31","article-title":"An empirical study on evaluation metrics of generative adversarial networks","author":"Xu","year":"2018","journal-title":"arXiv:1806.07755"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2109730"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3030910"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/DRBC.2015.11"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/9717300\/09783155.pdf?arnumber=9783155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T03:25:42Z","timestamp":1706757942000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9783155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/tim.2022.3178483","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}