{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:05:02Z","timestamp":1774641902153,"version":"3.50.1"},"reference-count":56,"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"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["50276068"],"award-info":[{"award-number":["50276068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51206181"],"award-info":[{"award-number":["51206181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2019.2961742","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T15:25:37Z","timestamp":1577114737000},"page":"3144-3158","source":"Crossref","is-referenced-by-count":28,"title":["Steady-State Process Fault Detection for Liquid Rocket Engines Based on Convolutional Auto-Encoder and One-Class Support Vector Machine"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0218-3253","authenticated-orcid":false,"given":"Xiaobin","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-0568","authenticated-orcid":false,"given":"Yuqiang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2095-8649","authenticated-orcid":false,"given":"Jianjun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4096-6902","authenticated-orcid":false,"given":"Runsheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3818-2159","authenticated-orcid":false,"given":"Xing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref38","first-page":"203","article-title":"Theories and Applications of Auto-Encoder Neural Networks: A Literature Survey","volume":"42","author":"yuan","year":"2019","journal-title":"Jisuanji Xuebao\/Chinese Journal of Computers"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2005.01.009"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ASMC.2005.1438783"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1017\/S026988891300043X"},{"key":"ref30","first-page":"849","article-title":"Development and application of big data technology in aviation industry","volume":"57","author":"liu","year":"2017","journal-title":"Telecommun Eng"},{"key":"ref37","first-page":"52","article-title":"Stacked convolutional auto-encoders for hierarchical feature extraction","author":"masci","year":"2011","journal-title":"Proc ICANN"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0151-9"},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.3390\/e18090322","article-title":"Mechanical fault diagnosis of high voltage circuit breakers based on wavelet time-frequency entropy and one-class support vector machine","volume":"18","author":"huang","year":"2016","journal-title":"Entropy"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2009.07.011"},{"key":"ref28","first-page":"28","article-title":"Civil aviation engine health condition monitoring based on DBN deep learning theory","volume":"25","author":"wu","year":"2017","journal-title":"Computer Measurement & Control"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2900295"},{"key":"ref29","first-page":"621","article-title":"Fault fusion diagnosis of aircraft engine based on deep learning","volume":"44","author":"che","year":"2018","journal-title":"J Beijing Univ Aeronaut Astronaut"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.2514\/6.1984-1286"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2018.03.001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2016.7819786"},{"key":"ref22","first-page":"1345","article-title":"Deep learning for fault diagnosis: The state of the art and challenge","volume":"32","author":"ren","year":"2017","journal-title":"Kongzhi yu Juece\/Control and Decision"},{"key":"ref21","first-page":"643","article-title":"Deep learning for control: The state of the art and prospects","volume":"42","author":"duan","year":"2016","journal-title":"ACTA Automatica Sinica"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2019.03.072"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.024"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ChiCC.2016.7554387"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4271\/2013-01-2090"},{"key":"ref50","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"glorot","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref51","article-title":"An overview of gradient descent optimization algorithms","author":"ruder","year":"2016","journal-title":"arXiv 1609 04747"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00747"},{"key":"ref55","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref54","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015435"},{"key":"ref52","article-title":"Adam: A method for stochastic gradient descent","author":"kingma","year":"2015","journal-title":"Proc ICLR"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"hinton","year":"2006","journal-title":"Science"},{"key":"ref40","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv 1603 04467"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref14","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1134\/S1054661816010065"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2018.2840738"},{"key":"ref18","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"collobert","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.050"},{"key":"ref4","first-page":"6131","article-title":"Inductive monitoring system based fault detection for liquid-propellant rocket engines","author":"gao","year":"2015","journal-title":"Proc CCC"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2514\/6.1990-1987"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2417501"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.012"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2004.05.070"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2012.09.004"},{"key":"ref49","first-page":"10","article-title":"Implementation of real-time fault detection algorithms based on neural network for liquid propellant rocket engines","volume":"29","author":"huang","year":"2007","journal-title":"J Nat'l Univ of Defense Technology"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S0098-1354(02)00162-X"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(00)00112-4"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.022"},{"key":"ref48","first-page":"19","article-title":"Implementation of fault detection and alarm system based on ATA algorithm in liquid rocket engine","volume":"31","author":"xie","year":"2005","journal-title":"Journal of Rocket Propulsion"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.5772\/7544"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"ref41","article-title":"TensorFlow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"arXiv 1605 08695"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICISA.2014.6847442"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-008-9082-5"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08939409.pdf?arnumber=8939409","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:35:58Z","timestamp":1641987358000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8939409\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2961742","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}