{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:26:24Z","timestamp":1778347584521,"version":"3.51.4"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"A*STAR Industrial Internet of Things Research Program","award":["RIE2020 IAF-PP"],"award-info":[{"award-number":["RIE2020 IAF-PP"]}]},{"name":"A*STAR Industrial Internet of Things Research Program","award":["A1788a0023"],"award-info":[{"award-number":["A1788a0023"]}]},{"DOI":"10.13039\/501100013290","name":"National Key Research and Development Program of China Stem Cell and Translational Research","doi-asserted-by":"publisher","award":["2017YFA0700900"],"award-info":[{"award-number":["2017YFA0700900"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013290","name":"National Key Research and Development Program of China Stem Cell and Translational Research","doi-asserted-by":"publisher","award":["2017YFA0700903"],"award-info":[{"award-number":["2017YFA0700903"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976200"],"award-info":[{"award-number":["61976200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Electron."],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1109\/tie.2021.3057030","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T08:01:31Z","timestamp":1612944091000},"page":"2022-2032","source":"Crossref","is-referenced-by-count":118,"title":["KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction"],"prefix":"10.1109","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9202-1073","authenticated-orcid":false,"given":"Qing","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8493-0712","authenticated-orcid":false,"given":"Keyu","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-5575","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2515054"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2010.11.018"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711422"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2336616"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2677334"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2623260"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2881543"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-009-0356-9"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2868687"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2924605"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/72.554195"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-32025-0_14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2844856"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2891463"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2972443"},{"key":"ref19","article-title":"Compressing deep convolutional networks using vector quantization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gong","year":"2014"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.521"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765695"},{"key":"ref22","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"Han"},{"key":"ref23","first-page":"2148","article-title":"Predicting parameters in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Denil","year":"2013"},{"key":"ref24","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Denton","year":"2014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00035"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_13"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref29","article-title":"Distilling the knowledge in a neural network","volume-title":"Proc. NIPS Deep Learn. Representation Learn. Workshop","volume":"1050","author":"Hinton","year":"2015"},{"key":"ref30","article-title":"Fitnets: Hints for thin deep nets","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Romero","year":"2015"},{"key":"ref31","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Zagoruyko","year":"2017"},{"key":"ref32","article-title":"Contrastive representation distillation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tian","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2931656"},{"key":"ref34","first-page":"742","article-title":"Learning efficient object detection models with knowledge distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref37","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tarvainen","year":"2017"},{"key":"ref38","first-page":"2018","article-title":"Born again neural networks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Furlanello","year":"80"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582798"},{"key":"ref41","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2935987"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3032690"}],"container-title":["IEEE Transactions on Industrial Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/41\/9594638\/09351733.pdf?arnumber=9351733","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:25:15Z","timestamp":1704846315000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9351733\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":44,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tie.2021.3057030","relation":{},"ISSN":["0278-0046","1557-9948"],"issn-type":[{"value":"0278-0046","type":"print"},{"value":"1557-9948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2]]}}}