{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:28:42Z","timestamp":1776331722598,"version":"3.50.1"},"reference-count":118,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,7]]},"DOI":"10.1109\/etfa45728.2021.9613682","type":"proceedings-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:13:36Z","timestamp":1638317616000},"page":"1-8","source":"Crossref","is-referenced-by-count":51,"title":["Challenges of machine learning-based RUL prognosis: A review on NASA's C-MAPSS data set"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7074-7797","authenticated-orcid":false,"given":"Simon","family":"Vollert","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0746-0424","authenticated-orcid":false,"given":"Andreas","family":"Theissler","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2019.8929267"},{"key":"ref38","article-title":"Remaining useful lifetime prediction via deep domain adaptation","volume":"195","author":"da costa","year":"2019","journal-title":"Reliability Engineering & System Safety"},{"key":"ref33","article-title":"A novel deep capsule neural network for remaining useful life estimation","volume":"234","author":"ruiz-tagle palazuelos","year":"2019","journal-title":"Journal of Risk and Reliability"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106344"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919566"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1049\/ip-cta:20050074"},{"key":"ref37","article-title":"Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network","author":"cheng","year":"2020","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"ref36","author":"kitchenham","year":"0","journal-title":"Procedures for Performing Systematic Reviews Keele University Technical Report TR\/SE-0401"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107257"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.076"},{"key":"ref28","article-title":"Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets","volume":"5","author":"ramasso","year":"2014","journal-title":"International Journal of Prognostics and Health Management"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2016.7819786"},{"key":"ref29","author":"zschech","year":"2019","journal-title":"Towards a Taxonomic Benchmarking Framework for Predictive Maintenance The Case of NASA's Turbofan Degradation"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/DEMPED.2019.8864915"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2019.2907402"},{"key":"ref21","article-title":"Deep Learning Algorithms for Bearing Fault Diagnostics-A Comprehensive Review","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2695583"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.088"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-019-0928-3"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106024"},{"key":"ref100","article-title":"A Joint Long Short-Term Memory and AdaBoost regression approach with application to remaining useful life estimation","author":"zhu","year":"2020","journal-title":"Measurement"},{"key":"ref25","article-title":"Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics","volume":"56","author":"jimenez","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2900295"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187053"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2018.03.262"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/app8122416"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106351"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/SAFEPROCESS45799.2019.9213408"},{"key":"ref55","article-title":"Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network","volume":"97","author":"wu","year":"2019","journal-title":"ISA Transactions"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.05.063"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Chongqing.2018.00184"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s12204-018-2027-5"},{"key":"ref40","article-title":"Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture","volume":"183","author":"ellefsen","year":"2019","journal-title":"Reliability Engineering & System Safety"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2017.8104917"},{"key":"ref3","author":"theissler","year":"2013","journal-title":"Detecting anomalies in multivariate time series from automotive systems"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2018.12.011","article-title":"Digitalizing Swedish industry: What is next?","volume":"105","author":"g\u00fcrd\u00fcr","year":"2019","journal-title":"Computers in Industry"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1396"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA46521.2020.9211903"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2891463"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/RAMS.2019.8768982"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.052"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.113100"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp48618.2020.00-98"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187043"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICASI.2018.8394326"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107788"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187040"},{"key":"ref72","article-title":"Hybrid Deep Neural Network Model for Remaining Useful Life Estimation","author":"aldulaimi","year":"0","journal-title":"International Conference on Acoustics Speech and Signal Processing (ICASSP)"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819435"},{"key":"ref70","article-title":"A joint classification-regression method for multi-stage remaining useful life prediction","volume":"58","author":"wu","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Paris.2019.00030"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206739"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.3390\/app9194156"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.103182"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.3390\/s20247109"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3020356"},{"key":"ref60","article-title":"Long short-term memory for machine remaining life prediction","volume":"48","year":"2018","journal-title":"Journal of Manufacturing Systems"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.06.004"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI44817.2019.9002732"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Qingdao46334.2019.8942857"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106113"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976595"},{"key":"ref67","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-32025-0_14","article-title":"Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life","volume":"9642","author":"babu","year":"2016","journal-title":"Database Systems for Advanced Applications LNCS"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106330"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107864"},{"key":"ref69","article-title":"Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines","author":"wang","year":"2020","journal-title":"Aerospace Systems"},{"key":"ref1","article-title":"Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems","volume":"22","author":"alc\u00e1cer","year":"2019","journal-title":"engineering science and technology"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106474"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582798"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569915"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2983760"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.3390\/app10031062"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2925634"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727410"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.013"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.016"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-018-2874-0"},{"key":"ref104","article-title":"An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme","volume":"199","year":"2020","journal-title":"Reliability Engineering & System Safety"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-5204-x"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.05.005"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103849"},{"key":"ref111","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","volume":"107","author":"khan","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"ref112","article-title":"Data-Driven Fault Diagnostics and Prognostics for Predictive Maintenance: A Brief Overview","author":"xu","year":"0","journal-title":"In IEEE 15th International Conference on Automation Science and Engineering (CASE)"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107028"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107241"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.12.017"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.106727"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.3390\/s20010176"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prog-nostics and health management design for rotary machinery systems-Reviews, methodology and applications","volume":"42","author":"lee","year":"2014","journal-title":"Mechanical Systems and Signal Processing"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref12","article-title":"Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset","author":"ramasso","year":"0","journal-title":"International Conference on Prognostics and Health Management"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.02.004"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2020.06.014"},{"key":"ref15","article-title":"Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo","author":"benker","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"key":"ref118","article-title":"VIAL-AD: Visual Interactive Labelling for Anomaly Detection - An approach and open research questions","author":"theissler","year":"0","journal-title":"4th International Workshop on Interactive Adaptive Learning CEUR-WS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711422"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.01.006"},{"key":"ref117","article-title":"Cluster-Clean-Label: An interactive Machine Learning approach for labeling high-dimensional data","author":"beil","year":"0","journal-title":"International Symposium on Visual Information Communication and Interaction"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11465-018-0472-3"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3318299.3318325"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.050"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103332"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.06.015"},{"key":"ref83","article-title":"Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks","author":"gugulothu","year":"2017","journal-title":"ArXiv"},{"key":"ref114","article-title":"Learning under concept drift: A review","volume":"31","author":"lu","year":"2018","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"ref113","article-title":"Interpretable Machine Learning: A brief survey from the predictive maintenance perspective","author":"vollert","year":"0","journal-title":"IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)"},{"key":"ref116","doi-asserted-by":"crossref","DOI":"10.1007\/s00371-018-1500-3","article-title":"VIAL: a unified process for visual interactive labeling","volume":"34","author":"bernard","year":"2018","journal-title":"The Visual Computer"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106987"},{"key":"ref115","article-title":"Matching Networks for One Shot Learning","volume":"29","author":"vinyals","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref89","article-title":"Prediction of Remaining Useful Life of turbofan engine using machine learning","author":"mathew","year":"0","journal-title":"Proceedings of the International Conference on Circuits and Systems"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/BRACIS.2019.00081"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Shanghai49105.2020.9281004"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107098"},{"key":"ref88","article-title":"A Light Gradient Boosting Machine for Re-mainning Useful Life Estimation of Aircraft Engines","author":"li","year":"0","journal-title":"International Conference on Intelligent Transportation Systems"}],"event":{"name":"2021 IEEE 26th International Conference on Emerging Technologies and Factory Automation (ETFA)","location":"Vasteras, Sweden","start":{"date-parts":[[2021,9,7]]},"end":{"date-parts":[[2021,9,10]]}},"container-title":["2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9613137\/9613141\/09613682.pdf?arnumber=9613682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:52:33Z","timestamp":1652201553000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9613682\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,7]]},"references-count":118,"URL":"https:\/\/doi.org\/10.1109\/etfa45728.2021.9613682","relation":{},"subject":[],"published":{"date-parts":[[2021,9,7]]}}}