{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:04:40Z","timestamp":1785470680555,"version":"3.56.0"},"reference-count":220,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Research Support Fund (RSF) of Symbiosis International (Deemed University), Pune, India"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3101284","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T20:23:55Z","timestamp":1627676635000},"page":"110255-110286","source":"Crossref","is-referenced-by-count":137,"title":["Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-5350","authenticated-orcid":false,"given":"Sameer","family":"Sayyad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-0952","authenticated-orcid":false,"given":"Satish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1942-9179","authenticated-orcid":false,"given":"Arunkumar","family":"Bongale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7597-0197","authenticated-orcid":false,"given":"Pooja","family":"Kamat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4903-1540","authenticated-orcid":false,"given":"Shruti","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.3390\/app8122416"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2020.10.108"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1115\/1.4049537"},{"key":"ref174","first-page":"467","article-title":"Crafting adversarial examples for deep learning based prognostics","author":"mode","year":"2020","journal-title":"Proc 19th IEEE Int Conf Mach Learn Appl (ICMLA)"},{"key":"ref173","first-page":"1","article-title":"Demystifying artificial intelligence based digital twins in manufacturing&#x2014;A bibliometric analysis of trends and techniques","author":"kumar","year":"2020","journal-title":"Library Philosophy & Practice"},{"key":"ref176","doi-asserted-by":"crossref","first-page":"11","DOI":"10.36001\/phmconf.2020.v12i1.1182","article-title":"Overcoming adversarial perturbations in data-driven prognostics through semantic structural context-driven deep learning","volume":"12","author":"zhou","year":"2020","journal-title":"Annual Conference of the PHM Society"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3022323"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.106682"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3032690"},{"key":"ref168","article-title":"Transfer learning for remaining useful life prediction based on consensus self-organizing models","author":"fan","year":"2019","journal-title":"arXiv 1909 07053"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109287"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2016.03.001"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2018.01.002"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CBI.2018.00028"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.09.015"},{"key":"ref31","year":"2015","journal-title":"Condition Monitoring and Diagnostics of Machines&#x2014;Prognostics&#x2014;Part 1 General Guidelines"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840831"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.01.014"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2003.11.006"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.101974"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2697842"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01273"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1063\/1.5031520"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO49542.2019.8961501"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053475"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1288\/1\/012075"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.101981"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ48607.2020.9177537"},{"key":"ref188","article-title":"Explainable artificial intelligence: A systematic review","author":"vilone","year":"2020","journal-title":"arXiv 2006 00093"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103678"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.008"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-018-1768-5"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmrt.2019.10.031"},{"key":"ref179","first-page":"1","article-title":"Unsupervised domain adaptation based remaining useful life prediction of rolling element bearings","volume":"5","author":"liu","year":"2020","journal-title":"Phme"},{"key":"ref29","first-page":"1","author":"wang","year":"2019","journal-title":"Automatic Visual Inspection and Condition-Based Maintenance for Catenary"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/1-84628-269-1_1"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2018.01.009"},{"key":"ref24","author":"gordon","year":"2019","journal-title":"Visual Inspection of Couplings and Machinery Components"},{"key":"ref23","author":"haarman","year":"2017","journal-title":"Predictive maintenance 4 0 Predict the unpredictable"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/FENDT.2013.6635554"},{"key":"ref25","author":"azab","year":"2019","journal-title":"Visual Inspection Practices of Cleaned Equipment Part I"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIC.2018.8782406"},{"key":"ref51","first-page":"1474","article-title":"Enabling of predictive maintenance in the brownfield through low-cost sensors, an IIoT-architecture and machine learning","author":"strauss","year":"2018","journal-title":"Proc IEEE Int Conf Big Data (Big Data)"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3008223"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-018-1428-5"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2900295"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107461"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.064"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.01.006"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ICRMS.2018.00067"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-8548-x"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/RACE.2015.7097283"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2809681"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-019-04464-w"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2010.2054172"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2013.2293234"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2008.03.026"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2016.2570568"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3901\/CJME.2012.01.160"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2010.11.018"},{"key":"ref53","author":"peter","year":"2020","journal-title":"Downtime in Manufacturing What&#x2019;s the True Cost"},{"key":"ref52","author":"nettleton","year":"2014","journal-title":"Commercial Data Mining"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/B978-075067531-4\/50006-3"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59003-1_26"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.3390\/s20226626"},{"key":"ref165","article-title":"Using explainable artificial intelligence to interpret remaining useful life using explainable artificial intelligence to interpret remaining useful life estimation with gated recurrent unit","author":"baptista","year":"2020"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9601389"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3046036"},{"key":"ref162","first-page":"1","article-title":"Generative adversarial network-based missing data handling and remaining useful life estimation for smart train control and monitoring systems","volume":"2020","author":"lee","year":"2020","journal-title":"J Adv Transp"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1520\/SSMS20170006"},{"key":"ref160","article-title":"Tool wear dataset of NUAA_Ideahouse","author":"li","year":"2021","journal-title":"IEEE Dataport"},{"key":"ref4","author":"sondalini","year":"2021","journal-title":"Business Article&#x2014;Equipment Failure and the Cost of Failure"},{"key":"ref3","first-page":"1","author":"santhanam","year":"2018","journal-title":"The US Cutting-Tools Market What Changes Lie Ahead"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2004.08.016"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84996-450-0_1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3060395"},{"key":"ref159","year":"2010","journal-title":"PHM Society Conference Data Challenge"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819400"},{"key":"ref49","article-title":"A survey of predictive maintenance: Systems, purposes and approaches","author":"ran","year":"2019","journal-title":"arXiv 1912 07383"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-020-1276-6"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2016.04.007"},{"key":"ref158","first-page":"1","article-title":"Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis","volume":"3","author":"zhang","year":"2021","journal-title":"Social Netw Appl Sci"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1108\/02632770210435161"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17906-3_10"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-215-5_4"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1108\/JQME-04-2016-0014"},{"key":"ref42","article-title":"Estimating remaining useful life in machines using artificial intelligence?: A scoping review","author":"sayyad","year":"0","journal-title":"Library Philosophy & Practice"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2299152"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/B978-075067531-4\/50003-8"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-5273(00)00067-0"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmapro.2016.03.010"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2013.07.015"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2016.05.010"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.06.001"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.03.012"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.3390\/machines9010011"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2019.05.386"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-017-0292-3"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2010.02.010"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2017.10.033"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1117\/12.2233204"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2016.11.044"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.11.1.64"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2587754"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.06.015"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/s11740-017-0729-4"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataCongress.2018.00028"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1115\/MSEC2017-2679"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1049\/iet-smt.2016.0423"},{"key":"ref197","article-title":"A survey on contrastive self-supervised learning","author":"jaiswal","year":"2020","journal-title":"arXiv 2011 00362"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01500-0"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-32156-1_5"},{"key":"ref193","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"wachter","year":"2018","journal-title":"Harv J Law Technol"},{"key":"ref194","author":"mcgrath","year":"2019","journal-title":"Interpreting AI &#x2019;Black Boxes&#x2019; With Counterfactual Explanations"},{"key":"ref195","year":"2018","journal-title":"Transfer Learning Explained"},{"key":"ref196","article-title":"Self-supervised visual feature learning with deep neural networks: A survey","author":"jing","year":"2019","journal-title":"arXiv 1902 06162"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1177\/0954405412473906"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2016.04.101"},{"key":"ref191","article-title":"Metrics for explainable AI: Challenges and prospects","author":"hoffman","year":"2018","journal-title":"arXiv 1812 04608"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-011-3536-7"},{"key":"ref192","first-page":"1","article-title":"A multidisciplinary survey and framework for design and evaluation of explainable AI systems","volume":"1","author":"mohseni","year":"2020","journal-title":"ACM Trans Interact Intell Syst"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-010-2907-9"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.02.004"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2004.05.003"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirpj.2008.09.007"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2017.02.294"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.12.037"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2016.12.009"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-3270(03)00096-7"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/S0890-6955(98)00020-0"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-018-2341-y"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/3516.974863"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2004.04.021"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-014-5679-9"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.3390\/s20164377"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/S0890-6955(01)00108-0"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.06.010"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-013-5258-5"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1109\/ICNSC.2018.8361285"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-016-1221-2"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1080\/0951192X.2018.1550681"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.05.149"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1080\/0951192X.2019.1686173"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.101958"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.15302\/J-ENG-2015054"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890566"},{"key":"ref207","year":"2018","journal-title":"Nebula Ai (NBAI)&#x2014;Decentralized ai Blockchain Whitepaper"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905689"},{"key":"ref205","author":"catak","year":"2020","journal-title":"Adversarial Machine Learning Mitigation Adversarial Learning"},{"key":"ref206","first-page":"280","article-title":"Deep neural network based malicious network activity detection under adversarial machine learning attacks","author":"catak","year":"2021","journal-title":"Proc Int Conf Intell Technol Appl"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044248"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.3390\/app9102078"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1177\/2470547017747553"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2020.03.051"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2020.05.163"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106600"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/CIST.2016.7804958"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1115\/MSEC2013-1106"},{"key":"ref218","article-title":"Physics-guided neural networks (PGNN): An application in lake temperature modeling","author":"karpatne","year":"2017","journal-title":"arXiv 1710 11431"},{"key":"ref219","first-page":"1","article-title":"Federated deep learning: A conceptual model and applied framework for industry 4.0","author":"elnagar","year":"2020","journal-title":"Proc 26th Amer Conf Inf Syst (AMCIS)"},{"key":"ref220","year":"2018","journal-title":"Federated Learning"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.027"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01526-4"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-011-3703-x"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.10.010"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.04.008"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3813029"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2924605"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.3390\/app9214500"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.3390\/app10041245"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2078296"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.011"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2844341"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.3390\/w11071387"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2805189"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.045"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488598000094"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569661"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.3390\/info11020106"},{"key":"ref144","author":"agogino","year":"2007","journal-title":"Mill Data Set"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-007-0948-5"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-4670-4_4"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.01.038"},{"key":"ref109","article-title":"Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning","volume":"173","author":"kunto?lu","year":"2021","journal-title":"Measurement"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/IECON.2012.6389448"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-016-1209-y"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.02.036"},{"key":"ref105","first-page":"3","article-title":"Data driven models for prognostics of high speed milling cutters","volume":"12","author":"jain","year":"2016","journal-title":"Int J Performability Eng"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.3390\/s16060795"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-017-0637-1"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-9711-0"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99713-1_7"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.3390\/s18113866"},{"key":"ref110","first-page":"108","article-title":"A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends","volume":"21","author":"kunto","year":"2021","journal-title":"SENSORS"},{"key":"ref10","year":"2015","journal-title":"Condition Monitoring and Diagnostics of Machines&#x2014;Prognostics&#x2014;Part 1 General Guidelines"},{"key":"ref11","first-page":"1","article-title":"From monitoring data to remaining useful life?: An evolving approach including uncertainty","author":"el koujok","year":"2008","journal-title":"Proc 34th Eur Saf Rel Data Assoc ESReDA Seminar 2nd Joint ESReDA\/ESRA Seminar Supporting Technol Adv Maintenance Informaiton Manage"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2009.11.010"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2010.07.003"},{"key":"ref14","year":"2021","journal-title":"Prescriptive and Predictive Analytics Markett&#x2014;Forecast(2021&#x2013;2026)"},{"key":"ref15","author":"rapoza","year":"2016","journal-title":"Maintaining Virtual System Uptime in Today&#x2019;s Transforming IT Infrastructure"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-007-1034-8"},{"key":"ref16","year":"2006","journal-title":"Downtime Costs Auto Industry 22k\/Minute&#x2014;Survey"},{"key":"ref117","first-page":"111","article-title":"Use of discrete wavelet features and support vector machine for fault diagnosis of face milling tool","volume":"12","author":"madhusudana","year":"2018","journal-title":"Structural Durability & Health Monitoring"},{"key":"ref17","author":"gallichan","year":"2017","journal-title":"After the Fall Cost Causes and Consequences of Unplanned Downtime"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.19026\/rjaset.7.502"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2006.12.007"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-004-2038-2"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-020-06129-5"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/ICEE-B.2017.8192142"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.06.026"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.3390\/s18030823"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1016\/j.precisioneng.2015.06.007"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2014.01.004"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2012.02.015"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-013-0774-6"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09502093.pdf?arnumber=9502093","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:18Z","timestamp":1639771038000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9502093\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":220,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3101284","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}