{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:57:32Z","timestamp":1772981852026,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Failures in safety\u2010critical systems such as aircraft engines pose severe economic and societal risks. This study introduces a novel Remaining Useful Life (RUL) prediction method uniquely combining diverse techniques. Specifically, the proposed methodology integrates fuzzy time series analysis with sliding window segmentation and Multinomial Naive Bayes (MNB) classification. These techniques transform raw sensor data from NASA's C\u2010MAPSS turbofan engine datasets into a symbolic representation that effectively captures degradation patterns leading to system failure. Tested across the four subsets\u2014FD001, FD002, FD003 and FD004\u2014from the C\u2010MAPSS NASA dataset, the proposed approach achieved competitive RMSE values of 24.73, 36.03, 34.71 and 39.07, respectively, while demonstrating robust PHM score metrics of as low as 1508 for one of the datasets. By optimising key parameters to enhance accuracy and computational efficiency, this low\u2010computational\u2010cost alternative to conventional deep learning models significantly advances RUL prediction, offering a promising alternative prognostic strategy in environments where the balance between computational efficiency and accuracy is essential.<\/jats:p>","DOI":"10.1111\/exsy.70202","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T04:50:52Z","timestamp":1767934252000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remaining Useful Life Prediction in an Aerospace Engine: A Multivariable Fuzzy Time Series Classification Approach"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5911-0544","authenticated-orcid":false,"given":"Luiz Rog\u00e9rio","family":"de Freitas J\u00fanior","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Universidade Federal de Minas Gerais  Belo Horizonte Minas Gerais Brazil"}]},{"given":"Frederico Gadelha","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Department of Computer Science Universidade Federal de Minas Gerais  Belo Horizonte Minas Gerais Brazil"}]}],"member":"311","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3527156"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742\u20106596\/1192\/1\/012003"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3203406"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2023.1356"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109756"},{"key":"e_1_2_13_7_1","unstructured":"Ba\u015fyi\u011fit A. C.Ulu andM.G\u00fczelkaya.2014.\u201cA New Fuzzy Time Series Model Using Triangular and Trapezoidal Membership Functions.\u201din International Conference on Time Series and Forecasting (ITISE)."},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2022.07.353"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(95)00220-0"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2019.2922152"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14653-1"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0206-3"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20226626"},{"key":"e_1_2_13_14_1","doi-asserted-by":"crossref","unstructured":"Hong C. W. K.Lee M. S.Ko J. K.Kim K.Oh andK.Hur.2020.\u201cMultivariate Time Series Forecasting for Remaining Useful Life of Turbofan Engine Using Deep\u2010Stacked Neural Network and Correlation Analysis.\u201d","DOI":"10.1109\/BigComp48618.2020.00-98"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2012.721010"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107611"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"e_1_2_13_18_1","first-page":"293","article-title":"In Search of Suitable Fuzzy Membership Function in Prediction of Time Series Data","volume":"9","author":"Mandal S.","year":"2012","journal-title":"International Journal of Computer Science Issues"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-014-0933-4"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/jlpea11020023"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108383"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2018.07.008"},{"key":"e_1_2_13_23_1","first-page":"2825","article-title":"Scikit\u2010Learn: Machine Learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2019.2933787"},{"key":"e_1_2_13_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2021.100758"},{"key":"e_1_2_13_26_1","doi-asserted-by":"crossref","unstructured":"Sateesh Babu G. P.Zhao andX. L.Li.2016.\u201cDeep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life.\u201d","DOI":"10.1007\/978-3-319-32025-0_14"},{"key":"e_1_2_13_27_1","doi-asserted-by":"crossref","unstructured":"Saxena A. K.Goebel D.Simon andN.Eklund.2008.\u201cDamage Propagation Modeling for Aircraft Engine Run\u2010to\u2010Failure Simulation.\u201d","DOI":"10.1109\/PHM.2008.4711414"},{"key":"e_1_2_13_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107257"},{"key":"e_1_2_13_29_1","unstructured":"Silva P. C. d. L. e.2018.\u201cpyfts: Fuzzy Time Series for Python.\u201d"},{"key":"e_1_2_13_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2020.2980289"},{"key":"e_1_2_13_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165\u20100114(93)90372\u2010O"},{"key":"e_1_2_13_32_1","doi-asserted-by":"crossref","unstructured":"Srinivasan A. J. C.Andresen andA.Holst.2023.\u201cEnsemble Neural Networks for Remaining Useful Life (RUL) Prediction.\u201darXiv preprint arXiv:2309.12445.","DOI":"10.36001\/phmap.2023.v4i1.3611"},{"issue":"3","key":"e_1_2_13_33_1","first-page":"513","article-title":"Dlau: A Scalable Deep Learning Accelerator Unit on FPGA","volume":"36","author":"Wang C.","year":"2016","journal-title":"IEEE Transactions on Computer\u2010Aided Design of Integrated Circuits and Systems"},{"key":"e_1_2_13_34_1","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2020.584463"},{"key":"e_1_2_13_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-005-4258-6"},{"key":"e_1_2_13_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.103182"},{"key":"e_1_2_13_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5041-1_57"},{"key":"e_1_2_13_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9224813"},{"key":"e_1_2_13_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0019\u20109958(65)90241\u2010X"},{"key":"e_1_2_13_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2942991"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.70202","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/exsy.70202","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.70202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T08:31:38Z","timestamp":1772958698000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.70202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,8]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10.1111\/exsy.70202"],"URL":"https:\/\/doi.org\/10.1111\/exsy.70202","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"value":"0266-4720","type":"print"},{"value":"1468-0394","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,8]]},"assertion":[{"value":"2025-05-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70202"}}