{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:03:11Z","timestamp":1769752991591,"version":"3.49.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and\/or the nature of the data. A solution based on a novel recurrent version of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach.<\/jats:p>","DOI":"10.1093\/jigpal\/jzae023","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T15:38:04Z","timestamp":1711553884000},"page":"605-623","source":"Crossref","is-referenced-by-count":4,"title":["Recurrent variational autoencoder approach for remaining useful life estimation"],"prefix":"10.1093","volume":"32","author":[{"given":"Nahuel","family":"Costa","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Oviedo , Gij\u00f3n, 33203, Asturias, Spain, nahuelcosta@uniovi.es"}]},{"given":"Luciano","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Oviedo , Gij\u00f3n, 33203, Asturias, Spain, luciano@uniovi.es"}]}],"member":"286","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"2024072520071788700_ref1","first-page":"150","volume":"56","author":"Ali","year":"2015","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2024072520071788700_ref2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.jmsy.2020.06.014","article-title":"Anomaly monitoring improves remaining useful life estimation of industrial machinery","volume":"56","author":"Aydemir","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"key":"2024072520071788700_ref3","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.ress.2015.04.009","article-title":"Condition-based maintenance effectiveness for series\u2013parallel power generation system\u2014a combined Markovian simulation model","volume":"142","author":"Azadeh","year":"2015","journal-title":"Reliability Engineering & System Safety"},{"key":"2024072520071788700_ref4","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/978-3-319-32025-0_14","volume-title":"International Conference on Database Systems for Advanced Applications","author":"Babu","year":"2016"},{"key":"2024072520071788700_ref5","volume-title":"Proceedings of the 12th Python in Science Conference","author":"Bergstra","year":"2013"},{"key":"2024072520071788700_ref6","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neucom.2018.09.076","article-title":"Bidirectional handshaking LSTM for remaining useful life prediction","volume":"323","author":"Elsheikh","year":"2019","journal-title":"Neurocomputing"},{"key":"2024072520071788700_ref7","author":"Gregor","year":"2018"},{"key":"2024072520071788700_ref8","first-page":"1","author":"Heimes","year":"2008","journal-title":"2008 International Conference on Prognostics and Health Management"},{"key":"2024072520071788700_ref9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ress.2015.12.003","article-title":"Degradations analysis and aging modeling for health assessment and prognostics of PEMFC","volume":"148","author":"Jouin","year":"2016","journal-title":"Reliability Engineering & System Safety"},{"key":"2024072520071788700_ref10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/NAFIPS.2005.1548498","author":"Khawaja","year":"2005","journal-title":"NAFIPS 2005\u20132005 Annual Meeting of the North American Fuzzy Information Processing Society"},{"key":"2024072520071788700_ref11","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TIE.2016.2623260","article-title":"Direct remaining useful life estimation based on support vector regression","volume":"64","author":"Khelif","year":"2016","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2024072520071788700_ref12","first-page":"3581","volume-title":"Advances in Neural Information Processing Systems","author":"Kingma","year":"2014"},{"key":"2024072520071788700_ref13","author":"Kingma","year":"2019"},{"key":"2024072520071788700_ref14","doi-asserted-by":"crossref","first-page":"106113","DOI":"10.1016\/j.asoc.2020.106113","article-title":"Remaining useful life prediction using multi-scale deep convolutional neural network","volume":"89","author":"Li","year":"2020","journal-title":"Applied Soft Computing"},{"key":"2024072520071788700_ref15","doi-asserted-by":"crossref","first-page":"75464","DOI":"10.1109\/ACCESS.2019.2919566","article-title":"A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"2024072520071788700_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","article-title":"Remaining useful life estimation in prognostics using deep convolution neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliability Engineering & System Safety"},{"key":"2024072520071788700_ref17","doi-asserted-by":"crossref","first-page":"105843","DOI":"10.1016\/j.knosys.2020.105843","article-title":"Data alignments in machinery remaining useful life prediction using deep adversarial neural networks","volume":"197","author":"Li","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"2024072520071788700_ref18","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1109\/TIM.2019.2917735","article-title":"Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning","volume":"69","author":"Mao","year":"2019","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2024072520071788700_ref19","first-page":"1","volume-title":"2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","author":"Mart\u00ednez","year":"2013"},{"key":"2024072520071788700_ref20","doi-asserted-by":"crossref","first-page":"5023","DOI":"10.1109\/TII.2019.2900295","article-title":"Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks","volume":"15","author":"Miao","year":"2019","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2024072520071788700_ref21","doi-asserted-by":"crossref","first-page":"102282","DOI":"10.1016\/j.ijinfomgt.2020.102282","article-title":"Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management","volume":"57","author":"Nguyen","year":"2021","journal-title":"International Journal of Information Management"},{"key":"2024072520071788700_ref22","volume-title":"Proceedings of the Annual Conference of the PHM Society","author":"Pasa","year":"2019"},{"key":"2024072520071788700_ref23","volume-title":"NASA Ames Prognostics Data Repository","author":"Saxena","year":"2008"},{"key":"2024072520071788700_ref24","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1007\/s11042-017-5204-x","article-title":"A novel soft computing method for engine RUL prediction","volume":"78","author":"Singh","year":"2019","journal-title":"Multimedia Tools and Applications"},{"key":"2024072520071788700_ref25","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/WACV.2017.58","volume-title":"2017 IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Smith","year":"2017"},{"key":"2024072520071788700_ref26","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10845-009-0356-9","article-title":"An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring","volume":"23","author":"Tian","year":"2012","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2024072520071788700_ref27","first-page":"6306","author":"Van Den Oord","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2024072520071788700_ref28","doi-asserted-by":"crossref","first-page":"7496","DOI":"10.1109\/TIE.2020.3003649","article-title":"Multiscale convolutional attention network for predicting remaining useful life of machinery","volume":"68","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2024072520071788700_ref29","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/PHM-Chongqing.2018.00184","author":"Wang","year":"2018","journal-title":"2018 Prognostics and System Health Management Conference (PHM-Chongqing)"},{"key":"2024072520071788700_ref30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neucom.2017.05.063","article-title":"Remaining useful life estimation of engineered systems using vanilla LSTM neural networks","volume":"275","author":"Wu","year":"2018","journal-title":"Neurocomputing"},{"key":"2024072520071788700_ref31","doi-asserted-by":"crossref","first-page":"103182","DOI":"10.1016\/j.compind.2019.103182","article-title":"An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation","volume":"115","author":"Xia","year":"2020","journal-title":"Computers in Industry"},{"key":"2024072520071788700_ref32","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/ICEMI.2011.6037773","volume-title":"IEEE 2011 10th International Conference on Electronic Measurement & Instruments","author":"Xiongzi","year":"2011"},{"key":"2024072520071788700_ref33","first-page":"823","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Zhao","year":"2019"},{"key":"2024072520071788700_ref34","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ress.2017.02.007","article-title":"Remaining useful life prediction of aircraft engine based on degradation pattern learning","volume":"164","author":"Zhao","year":"2017","journal-title":"Reliability Engineering & System Safety"},{"key":"2024072520071788700_ref35","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/ICPHM.2017.7998311","author":"Zheng","year":"2017","journal-title":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)"}],"container-title":["Logic Journal of the IGPL"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/32\/4\/605\/58646577\/jzae023.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/32\/4\/605\/58646577\/jzae023.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T20:09:36Z","timestamp":1721938176000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jigpal\/article\/32\/4\/605\/7633927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,26]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,3,26]]},"published-print":{"date-parts":[[2024,7,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jigpal\/jzae023","relation":{},"ISSN":["1367-0751","1368-9894"],"issn-type":[{"value":"1367-0751","type":"print"},{"value":"1368-9894","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,8]]},"published":{"date-parts":[[2024,3,26]]}}}