{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T21:54:33Z","timestamp":1789077273523,"version":"build-2803163510"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2016YFC0201400"],"award-info":[{"award-number":["2016YFC0201400"]}]},{"name":"Provincial Key R&D Program of Zhejiang Province","award":["2017C03019"],"award-info":[{"award-number":["2017C03019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Joint Fund for Integrating of Informatization and Industrialization","award":["U1509217"],"award-info":[{"award-number":["U1509217"]}]},{"name":"International Science and Technology Cooperation Program of Zhejiang Province for Joint Research in High-tech Industry","award":["2016C54007."],"award-info":[{"award-number":["2016C54007."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Electron."],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1109\/tie.2018.2798633","type":"journal-article","created":{"date-parts":[[2018,1,26]],"date-time":"2018-01-26T19:14:52Z","timestamp":1516994092000},"page":"540-550","source":"Crossref","is-referenced-by-count":85,"title":["An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis"],"prefix":"10.1109","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6627-4598","authenticated-orcid":false,"given":"Zhixin","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2370-1016","authenticated-orcid":false,"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"crossref","DOI":"10.21595\/jve.2017.18373","article-title":"Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm","author":"zhang","year":"2017","journal-title":"J Vibroeng"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2755064"},{"key":"ref33","first-page":"1322","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"0","journal-title":"Proc IEEE Int Joint Conf IEEE World Congr Comput Intell"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1007\/11538059_91","article-title":"Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning","author":"han","year":"2005","journal-title":"J Adv Computat Intell"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.232"},{"key":"ref37","first-page":"1","article-title":"Identifying maximum imbalance in datasets for fault diagnosis of gearboxes","author":"santos","year":"2015","journal-title":"J Intell Manuf"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.10.012"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727770"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2066988"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.03.073"},{"key":"ref27","first-page":"3","article-title":"A synthetic minority oversampling method based on local densities in low-dimensional space for imbalanced learning","author":"xie","year":"0","journal-title":"Proc Database Syst Adv Appl"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"guo","year":"2017","journal-title":"Expert Syst Appl"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2017.2661250"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2519325"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.136"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5121\/ijdkp.2014.4602"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.008"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"ref23","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","author":"khan","year":"2015","journal-title":"arXiv preprint arXiv 1508 03422"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2014.2371472"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.11.014"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/0098-1354(93)80018-I"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cherd.2010.01.005"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2015.2445380"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.11.014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2012.144"},{"key":"ref13","first-page":"1","article-title":"Efficient sampling techniques for ensemble learning and diagnosing bearing defects under class imbalanced condition","author":"farajzadeh-zanjani","year":"0","journal-title":"Proc Comput Intell"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.01.061"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/cjce.22603"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2422833"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.06.024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.431"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/URAI.2016.7625791"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/j.asoc.2010.04.012","article-title":"Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process","volume":"11","author":"eslamloueyan","year":"2011","journal-title":"Appl Soft Comput"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.3390\/s150305627","article-title":"An SVM-based solution for fault detection in wind turbines","volume":"15","author":"santos","year":"2015","journal-title":"SENSORS"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.081"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.cjche.2016.04.015","article-title":"Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy","volume":"24","author":"guozhu","year":"2016","journal-title":"Chin J Chem Eng"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2017.02.041"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.11.031"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2683528"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.3390\/s16101695","article-title":"Fault diagnosis based on chemical sensor data with an active deep neural network","volume":"16","author":"jiang","year":"2016","journal-title":"SENSORS"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654926"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref47","first-page":"1","article-title":"Learning representations by back-propagating errors","volume":"5","author":"rumelhart","year":"1988","journal-title":"Cognitive Modeling"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2459678"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1492-5"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.431"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.07.040"}],"container-title":["IEEE Transactions on Industrial Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/41\/8453958\/08270615.pdf?arnumber=8270615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T19:25:02Z","timestamp":1719775502000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8270615\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":50,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tie.2018.2798633","relation":{},"ISSN":["0278-0046","1557-9948"],"issn-type":[{"value":"0278-0046","type":"print"},{"value":"1557-9948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}