{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T20:16:32Z","timestamp":1730232992068,"version":"3.28.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,14]]},"DOI":"10.1109\/iccais52680.2021.9624654","type":"proceedings-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T16:21:26Z","timestamp":1639066886000},"page":"740-745","source":"Crossref","is-referenced-by-count":0,"title":["Bearing Fault Diagnosis Method Based on Improved Convolutional Neural Network"],"prefix":"10.1109","author":[{"given":"Jiangyun","family":"Duan","sequence":"first","affiliation":[]},{"given":"Youming","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"KNN based fault diagnosis system for induction motor","author":"samanta","year":"0","journal-title":"International Conference on CONTROL"},{"key":"ref11","article-title":"Rolling bearing fault diagnosis using modified K-means cluster analysis","author":"xin","year":"0","journal-title":"Vibroengineering procedia"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06590-8_45"},{"key":"ref13","first-page":"164","volume":"69","author":"zhang","year":"2015","journal-title":"A novel bearing fault diagnosis model integrated permutation entropy ensemble empirical mode decomposition and optimized SVM"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.4028\/www.scientific.net\/AMR.986-987.1491","article-title":"Fault Diagnosis of Bearing Based on KPCA and KNN Method","volume":"986 987","author":"wang","year":"2014","journal-title":"Advanced Materials Research"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional neural network based fault detection for rotating machinery [J]","volume":"377","author":"wang","year":"2016","journal-title":"Journal of Sound and Vibration"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s12206-019-1007-5","article-title":"A new fault diagnosis method based on convolutional neural network and compressive sensing","volume":"33","author":"ma","year":"2019","journal-title":"Journal of Mechanical Science and Technology"},{"key":"ref17","first-page":"1","article-title":"Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network","volume":"2020","author":"guo","year":"2020","journal-title":"Shock and Vibration"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","article-title":"Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization","volume":"110","author":"jia","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CIT.2014.144"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2015.04.010"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s41601-016-0022-0"},{"key":"ref6","article-title":"Application of Fractional Fourier Transform in Fault Diagnostics of Rolling Bearing","author":"shao","year":"2017","journal-title":"Journal of Harbin University of Science and Technology"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.08.041"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2991\/jimec-17.2017.106"},{"key":"ref7","article-title":"Kernel parameter selection of RBM-SVM and its application in fault diagnosis","author":"zhou","year":"2014","journal-title":"Journal of Electronic Measurement and Instrumentation"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2013.6621447"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2011.09.008"},{"journal-title":"Bayesian networks in fault diagnosis Practice and application","year":"2019","author":"cai","key":"ref9"},{"key":"ref20","article-title":"Improvement of Convolutional Neural Network Structure for Image Steganalysis","author":"gao","year":"2018","journal-title":"Computer Engineering"}],"event":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","start":{"date-parts":[[2021,10,14]]},"location":"Xi'an, China","end":{"date-parts":[[2021,10,17]]}},"container-title":["2021 International Conference on Control, Automation and Information Sciences (ICCAIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9624464\/9624202\/09624654.pdf?arnumber=9624654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:53:46Z","timestamp":1652187226000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9624654\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/iccais52680.2021.9624654","relation":{},"subject":[],"published":{"date-parts":[[2021,10,14]]}}}