{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:22:05Z","timestamp":1774120925185,"version":"3.50.1"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2019.2952143","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T20:52:09Z","timestamp":1573159929000},"page":"2813-2823","source":"Crossref","is-referenced-by-count":3,"title":["Rapidly Learning Bayesian Networks for Complex System Diagnosis: A Reinforcement Learning Directed Greedy Search Approach"],"prefix":"10.1109","volume":"8","author":[{"given":"Wenfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic and Information Engineering, Beihang University, Beijing, China"}]},{"given":"Wenquan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1196-4089","authenticated-orcid":false,"given":"Hongbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, Beihang University, Beijing, China"}]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc ICLR"},{"key":"ref38","article-title":"An overview of gradient descent optimization algorithms","author":"ruder","year":"2016","journal-title":"arXiv 1609 04747"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2017.2661250"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.016"},{"key":"ref31","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","volume":"377","author":"janssens","year":"2016","journal-title":"J Sound Vib"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2018.12.011"},{"key":"ref37","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref36","article-title":"ADADELTA: An adaptive learning rate method","author":"zeiler","year":"2012","journal-title":"arXiv 1212 5701"},{"key":"ref35","volume":"43","author":"milani?","year":"2011","journal-title":"Numerical Optimization"},{"key":"ref34","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2012.11.007"},{"key":"ref40","volume":"25","author":"boyd","year":"2010","journal-title":"Convex optimization"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2695583"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.10.012"},{"key":"ref13","volume":"53","author":"koller","year":"2013","journal-title":"Probabilistic Graphical Models"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1214\/14-BA889"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1140\/epjst\/e2015-02349-9"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2016.03.003"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2015.03.003"},{"key":"ref19","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"arXiv 1703 03400"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2774777"},{"key":"ref4","article-title":"Model-and data-driven approaches to fault detection and isolation in complex systems","author":"khorasgani","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2017.2728371"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CCDC.2016.7531291"},{"key":"ref6","first-page":"1","article-title":"Survey on data driven fault diagnosis methods","volume":"26","author":"li","year":"2011","journal-title":"Kongzhi yu Juece\/Control and Decision"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2551940"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-6410-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2016.2542186"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2013.09.043"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2011.6160714"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2323212"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2018.03.024"},{"key":"ref20","author":"sutton","year":"2018","journal-title":"Reinforcement Learning An Introduction"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2016.10.007"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1561\/2200000071"},{"key":"ref42","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref24","volume":"16","author":"bishop","year":"2007","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.03.105"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511811357"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(96)00142-2"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref43","author":"paszke","year":"2017","journal-title":"PyTorch"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08894115.pdf?arnumber=8894115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T19:46:42Z","timestamp":1662666402000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8894115\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2952143","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}