{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:21:42Z","timestamp":1781104902842,"version":"3.54.1"},"reference-count":72,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system\u2019s state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.<\/jats:p>","DOI":"10.3390\/s24113382","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T08:30:22Z","timestamp":1716539422000},"page":"3382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6661-6754","authenticated-orcid":false,"given":"Gabriele","family":"Patrizi","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Martiri","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Polytechnic of Milan, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5696-1271","authenticated-orcid":false,"given":"Antonio","family":"Pievatolo","sequence":"additional","affiliation":[{"name":"Institute for Applied Mathematics and Information Technologies \u201cE. Magenes\u201d, National Research Council, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-5332","authenticated-orcid":false,"given":"Alessandro","family":"Magrini","sequence":"additional","affiliation":[{"name":"Department of Statistics, Computer Science, Applications \u201cG. Parenti\u201d, University of Florence, 50134 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6530-5644","authenticated-orcid":false,"given":"Giovanni","family":"Meccariello","sequence":"additional","affiliation":[{"name":"Institute of Sciences and Technologies for Energy and Sustainable Mobility, National Research Council, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8446-8203","authenticated-orcid":false,"given":"Loredana","family":"Cristaldi","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Polytechnic of Milan, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5948-5917","authenticated-orcid":false,"given":"Nedka Dechkova","family":"Nikiforova","sequence":"additional","affiliation":[{"name":"Department of Statistics, Computer Science, Applications \u201cG. Parenti\u201d, University of Florence, 50134 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","unstructured":"Saha, B., and Goebel, K. (2024, January 10). Battery Data Set, Nasa Ames Prognostic Data Repository. Available online: https:\/\/scirp.org\/reference\/referencespapers?referenceid=3297577."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1038\/s41560-019-0356-8","article-title":"Data-driven prediction of battery cycle life before capacity degradation","volume":"4","author":"Severson","year":"2019","journal-title":"Nat. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.joule.2019.11.018","article-title":"Battery lifetime prognostics","volume":"4","author":"Hu","year":"2020","journal-title":"Joule"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Patrizi, G., Picano, B., Catelani, M., Fantacci, R., and Ciani, L. (2022, January 16\u201319). Validation of RUL estimation method for battery prognostic under different fast-charging conditions. Proceedings of the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada.","DOI":"10.1109\/I2MTC48687.2022.9806707"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"A63","DOI":"10.1149\/1.3515902","article-title":"Mathematical modeling of lithium iron phosphate electrode: Galvanostatic charge\/discharge and path dependence","volume":"158","author":"Safari","year":"2010","journal-title":"J. Electrochem. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101932","DOI":"10.1016\/j.est.2020.101932","article-title":"Physics inspired model for estimating \u2018cycles to failure\u2019 as a function of depth of discharge for lithium ion batteries","volume":"33","author":"Deshpande","year":"2021","journal-title":"J. Energy Storage"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/00401706.1993.10485038","article-title":"Using degradation measures to estimate a time-to-failure distribution","volume":"35","author":"Lu","year":"1993","journal-title":"Technometrics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103888","DOI":"10.1016\/j.est.2021.103888","article-title":"Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation","volume":"46","author":"Kim","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"560726","DOI":"10.1155\/2014\/560726","article-title":"Accelerated degradation tests modeling based on the nonlinear wiener process with random effects","volume":"2014","author":"Tang","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"404","DOI":"10.4028\/www.scientific.net\/AMR.118-120.404","article-title":"Planning of step-stress accelerated degradation test with stress optimization","volume":"118","author":"Ge","year":"2010","journal-title":"Adv. Mater. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TR.2009.2033734","article-title":"Optimal step-stress accelerated degradation test plan for gamma degradation processes","volume":"58","author":"Tseng","year":"2009","journal-title":"IEEE Trans. Reliab."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/TR.2014.2315773","article-title":"Accelerated degradation test planning using the inverse Gaussian process","volume":"63","author":"Ye","year":"2014","journal-title":"IEEE Trans. Reliab."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14618","DOI":"10.1109\/TVT.2020.3032201","article-title":"A method of state-of-charge estimation for EV power lithium-ion battery using a novel adaptive extended Kalman filter","volume":"69","author":"He","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, L., Saldivar, A.A.F., Bai, Y., and Li, Y. (2019). Battery remaining useful life prediction with inheritance particle filtering. Energies, 12.","DOI":"10.3390\/en12142784"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Z., Sun, M., Shu, X., Xiao, R., and Shen, J. (2018). Online state of health estimation for lithium-ion batteries based on support vector machine. Appl. Sci., 8.","DOI":"10.3390\/app8060925"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108967","DOI":"10.1016\/j.asoc.2022.108967","article-title":"An optimal relevance vector machine with a modified degradation model for remaining useful lifetime prediction of lithium-ion batteries","volume":"124","author":"Guo","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, G., Lyu, Z., and Li, X. (2023). An Optimized Random Forest Regression Model for Li-Ion Battery Prognostics and Health Management. Batteries, 9.","DOI":"10.3390\/batteries9060332"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ismail, M., Dlyma, R., Elrakaybi, A., Ahmed, R., and Habibi, S. (2017, January 7\u201310). Battery state of charge estimation using an Artificial Neural Network. Proceedings of the 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Harbin, China.","DOI":"10.1109\/ITEC.2017.7993295"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8831","DOI":"10.1109\/TIM.2020.2996004","article-title":"Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Conditional Variational Autoencoders-Particle Filter","volume":"69","author":"Jiao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112093","DOI":"10.1016\/j.measurement.2022.112093","article-title":"Remaining useful life prediction of lithium-ion batteries based on attention mechanism and bidirectional long short-term memory network","volume":"204","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1016\/j.procs.2022.12.383","article-title":"A Remaining Useful Life Prediction Method for Lithium-ion Battery Based on Temporal Transformer Network","volume":"217","author":"Song","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"50587","DOI":"10.1109\/ACCESS.2018.2858856","article-title":"Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach","volume":"6","author":"Ren","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3524611","DOI":"10.1109\/TIM.2021.3111009","article-title":"Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network","volume":"70","author":"Catelani","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jechem.2023.03.026","article-title":"The development of machine learning-based remaining useful life prediction for lithium-ion batteries","volume":"82","author":"Li","year":"2023","journal-title":"J. Energy Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10314","DOI":"10.1016\/j.jpowsour.2011.08.040","article-title":"Prognostics of lithium-ion batteries based on Dempster\u2013Shafer theory and the Bayesian Monte Carlo method","volume":"196","author":"He","year":"2011","journal-title":"J. Power Sources"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.1109\/TIE.2017.2782224","article-title":"Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression","volume":"65","author":"Wei","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"30","DOI":"10.4271\/2015-01-9147","article-title":"Prediction of Lithium-ion battery\u2019s remaining useful life based on relevance vector machine","volume":"5","author":"Zhang","year":"2016","journal-title":"SAE Int. J. Altern. Powertrains"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","article-title":"Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Martiri, L., Azzalini, D., Flammini, B., Cristaldi, L., and Amigoni, F. (2023, January 25\u201327). Improving Remaining Useful Life Estimation of Lithium-Ion Batteries when Nearing End of Life. Proceedings of the 2023 IEEE MetroXRAINE, Milan, Italy.","DOI":"10.1109\/MetroXRAINE58569.2023.10405675"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19621","DOI":"10.1109\/ACCESS.2022.3151975","article-title":"Transformer network for remaining useful life prediction of lithium-ion batteries","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s10033-021-00668-y","article-title":"Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method","volume":"35","author":"Che","year":"2022","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107270","DOI":"10.1016\/j.est.2023.107270","article-title":"Capacity evaluation and degradation analysis of lithium-ion battery packs for on-road electric vehicles","volume":"65","author":"Liu","year":"2023","journal-title":"J. Energy Storage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105216","DOI":"10.1016\/j.est.2022.105216","article-title":"Understanding the Li-ion battery pack degradation in the field using field-test and lab-test data","volume":"53","author":"Mutagekar","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s44172-023-00153-5","article-title":"Degradation in parallel-connected lithium-ion battery packs under thermal gradients","volume":"3","author":"Chen","year":"2024","journal-title":"Commun. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ress.2015.03.036","article-title":"A random-effects model for long-term degradation analysis of solid oxide fuel cells","volume":"140","author":"Guida","year":"2015","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1002\/er.5002","article-title":"Remaining useful life prediction of lithium-ion battery based on extended Kalman particle filter","volume":"44","author":"Duan","year":"2020","journal-title":"Int. J. Energy Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/TCPMT.2015.2483783","article-title":"Reliability Prediction Using Physics\u2013Statistics-Based Degradation Model","volume":"5","author":"Xu","year":"2015","journal-title":"IEEE Trans. Compon. Packag. Manuf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neucom.2019.09.074","article-title":"Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression","volume":"376","author":"Xue","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100122","DOI":"10.1016\/j.ijoes.2023.100122","article-title":"Remaining useful life prediction of lithium-ion battery based on chaotic particle swarm optimization and particle filter","volume":"18","author":"Ye","year":"2023","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.microrel.2017.12.028","article-title":"Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery","volume":"81","author":"Duong","year":"2018","journal-title":"Microelectron. Reliab."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"54843","DOI":"10.1109\/ACCESS.2019.2913163","article-title":"Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106050","DOI":"10.1016\/j.est.2022.106050","article-title":"Particle swarm optimized data-driven model for remaining useful life prediction of lithium-ion batteries by systematic sampling","volume":"56","author":"Ansari","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4110","DOI":"10.1109\/TVT.2018.2864688","article-title":"Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles","volume":"68","author":"Xiong","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101310","DOI":"10.1016\/j.est.2020.101310","article-title":"Effect of current on cycle aging of lithium ion batteries","volume":"29","author":"Barcellona","year":"2020","journal-title":"J. Energy Storage"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Barcellona, S., Cristaldi, L., Faifer, M., Petkovski, E., Piegari, L., and Toscani, S. (2021, January 7\u20139). State of health prediction of lithium-ion batteries. Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0&IoT), Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488542"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1111\/j.2517-6161.1959.tb00338.x","article-title":"Optimum Experimental Designs","volume":"21","author":"Kiefer","year":"1959","journal-title":"J. R. Stat. Soc. Ser. Methodol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"363","DOI":"10.4153\/CJM-1960-030-4","article-title":"The equivalence of two extremum problems","volume":"12","author":"Kiefer","year":"1960","journal-title":"Can. J. Math."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02664760903406488","article-title":"Optimal design of accelerated degradation tests based on Wiener process models","volume":"38","author":"Lim","year":"2011","journal-title":"J. Appl. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.apm.2020.03.036","article-title":"Optimal plan for Wiener constant-stress accelerated degradation model","volume":"84","author":"Jiang","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_50","first-page":"213","article-title":"Optimal design of accelerated degradation test based on Gamma process models","volume":"29","author":"Guan","year":"2013","journal-title":"Chin. J. Appl. Probab. Stat."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.cie.2018.09.003","article-title":"Planning of step-stress accelerated degradation test based on non-stationary gamma process with random effects","volume":"125","author":"Duan","year":"2018","journal-title":"Comput. Ind. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ress.2017.03.010","article-title":"Optimal design of constant-stress accelerated degradation tests using the M-optimality criterion","volume":"164","author":"Wang","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"686","DOI":"10.4236\/ojs.2019.96044","article-title":"An Optimal Design of Accelerated Degradation Tests Based on Degradation Performance","volume":"9","author":"Wu","year":"2019","journal-title":"Open J. Stat."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.ejor.2014.09.003","article-title":"Optimum step-stress accelerated degradation test for Wiener degradation process under constraints","volume":"241","author":"Hu","year":"2015","journal-title":"Eur. J. Oper. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1080\/00401706.2012.715838","article-title":"Methods for planning repeated measures degradation studies","volume":"55","author":"Weaver","year":"2013","journal-title":"Technometrics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1002\/asmb.2061","article-title":"Methods for planning Accelerated Repeated Measures Degradation Tests (with discussion)","volume":"30","author":"Weaver","year":"2014","journal-title":"Appl. Stoch. Models Bus. Ind."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TR.2020.2995333","article-title":"Optimal setting of test conditions and allocation of test units for accelerated degradation tests with two stress variables","volume":"70","author":"Fang","year":"2020","journal-title":"IEEE Trans. Reliab."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1214\/ss\/1177009939","article-title":"Bayesian experimental design: A review","volume":"10","author":"Chaloner","year":"1995","journal-title":"Stat. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1111\/insr.12107","article-title":"A review of modern computational algorithms for Bayesian optimal design","volume":"84","author":"Ryan","year":"2016","journal-title":"Int. Stat. Rev."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/j.1467-9868.2007.00586.x","article-title":"An optimal experimental design criterion for discriminating between non-normal models","volume":"69","author":"Tommasi","year":"2007","journal-title":"J. R. Stat. Soc. Ser. Stat. Methodol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.csda.2009.07.022","article-title":"Bayesian optimum designs for discriminating between models with any distribution","volume":"54","author":"Tommasi","year":"2010","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/TR.2011.2170115","article-title":"Bayesian methods for accelerated destructive degradation test planning","volume":"61","author":"Shi","year":"2011","journal-title":"IEEE Trans. Reliab."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/JSEE.2015.00058","article-title":"Bayesian optimal design of step stress accelerated degradation testing","volume":"26","author":"Li","year":"2015","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_65","first-page":"5690","article-title":"A Bayesian optimal design for accelerated degradation testing based on the inverse Gaussian process","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/00401706.2019.1695676","article-title":"Bayesian methods for planning accelerated repeated measures degradation tests","volume":"63","author":"Weaver","year":"2021","journal-title":"Technometrics"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11222-021-10000-2","article-title":"Sequential Bayesian optimal experimental design for structural reliability analysis","volume":"31","author":"Agrell","year":"2021","journal-title":"Stat. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"107622","DOI":"10.1016\/j.cie.2021.107622","article-title":"A model-based reinforcement learning approach for maintenance optimization of degrading systems in a large state space","volume":"161","author":"Zhang","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ejor.2019.08.050","article-title":"A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters","volume":"282","author":"Omshi","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"ref_70","first-page":"299","article-title":"A review on degradation modelling and its engineering applications","volume":"13","author":"Shahraki","year":"2017","journal-title":"Int. J. Perform. Eng."},{"key":"ref_71","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1146\/annurev-control-042920-092451","article-title":"Partially observable markov decision processes and robotics","volume":"5","author":"Kurniawati","year":"2022","journal-title":"Annu. Rev. Control. Robot. Auton. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:11Z","timestamp":1760107691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":72,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113382"],"URL":"https:\/\/doi.org\/10.3390\/s24113382","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}