{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T14:56:42Z","timestamp":1767797802013,"version":"3.49.0"},"reference-count":41,"publisher":"Elsevier BV","issue":"17","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer aided Civil Eng"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Ensuring the safe, reliable, and cost\u2010efficient operation of transportation systems such as elevators is critical for the maintenance of civil infrastructures. The ability to monitor the health state and classify different operational states (elevator moving up\/down, stopped, doors opening\/closing) may lead to the development of intelligent solutions, such as diagnostics and predictive maintenance. Accordingly, downtime and maintenance costs can be significantly reduced with an accurate monitoring of the operation parameters and dynamics. In this context, this paper presents a novel approach for the operational state classification of elevator systems based on a one\u2010dimensional convolutional neural network, using exclusively a single axis (Z) of an accelerometer signal. The proposed model utilizes a single accelerometer and addresses the challenge of distinguishing overlapping signal patterns, such as those produced by vertical displacement and door movements. The approach includes an interpretability stage, which demonstrates the data processing involved in extracting features from the underlying physical phenomena captured in the acceleration signal. Obtained results have been validated with an on\u2010site captured dataset which contains 250 elevator journeys and compared with three other classification methods that have been conventionally used: generalized likelihood ratio test (GLRT), barometer\u2010assisted GLRT, and three conventional machine learning modelss. It has been shown that the proposed approach is very accurate, with 96% of the average F1 score and, importantly, includes the analytic relation of the classification model features.<\/jats:p>","DOI":"10.1111\/mice.13479","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:37:52Z","timestamp":1744418272000},"page":"2464-2479","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable operational state classification framework for elevators through convolutional neural networks"],"prefix":"10.1016","volume":"40","author":[{"given":"Jon","family":"Olaizola","sequence":"first","affiliation":[{"name":"Electronics and Computing Department Mondragon University Gipuzkoa Spain"}]},{"given":"Unai","family":"Izagirre","sequence":"additional","affiliation":[{"name":"Electronics and Computing Department Mondragon University Gipuzkoa Spain"}]},{"given":"Oscar","family":"Serradilla","sequence":"additional","affiliation":[{"name":"Treasury Laboral Kutxa Gipuzkoa Spain"}]},{"given":"Ekhi","family":"Zugasti","sequence":"additional","affiliation":[{"name":"Electronics and Computing Department Mondragon University Gipuzkoa Spain"}]},{"given":"Mikel","family":"Mendicute","sequence":"additional","affiliation":[{"name":"Electronics and Computing Department Mondragon University Gipuzkoa Spain"}]},{"given":"Jose I.","family":"Aizpurua","sequence":"additional","affiliation":[{"name":"Computer Science and Artificial Intelligence Department University of the Basque Country (UPV\/EHU) Donostia Gipuzkoa Spain"},{"name":"Ikerbasque Basque Foundation for Science Gipuzkoa Spain"}]}],"member":"78","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"e_1_2_10_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2013.06.003"},{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733\u20109445(2006)132:1(102)"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781482281767"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2880930"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"Awatramani J. Verma G. Hasteer N. &Sindhwani R.(2022).Investigating strategies and parameters to predict maintenance of an elevator system. InV. V.Rao A.Kumaraswamy S.Kalra &A.Saxena(Eds.) Computational and experimental methods in mechanical engineering: Proceedings of ICCEMME 2021(pp.323\u2013332).Springer.","DOI":"10.1007\/978-981-16-2857-3_32"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.067"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.37418\/amsj.9.10.53"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCS.2015.2427045"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2928244"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.13287"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742\u20106596\/2010\/1\/012182"},{"key":"e_1_2_10_13_1","unstructured":"InvenSense T. D. K.(2020).DK\u201020789 data adquisition board.TDK InvenSense.https:\/\/invensense.tdk.com\/products\/dk\u201020789\/"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8667.2005.00399.x"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107965"},{"key":"e_1_2_10_16_1","volume-title":"Fundamentals of statistical signal processing: Estimation theory","author":"Kay S. M.","year":"1993"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-95717-3_51"},{"key":"e_1_2_10_18_1","unstructured":"Kone. (2024).Kone q3 2024.https:\/\/www.kone.com\/en\/investors\/reports\u2010and\u2010presentations\/financial\u2010reports\/"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-33383-0_5"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2023.3256080"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2020.3048950"},{"key":"e_1_2_10_22_1","doi-asserted-by":"crossref","unstructured":"Liono J. Abdallah Z. S. Qin A. K. &Salim F. D.(2018).Inferring transportation mode and human activity from mobile sensing in daily life.MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services New York(pp.342\u2013351).https:\/\/doi.org\/10.1145\/3286978.3287006","DOI":"10.1145\/3286978.3287006"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0203"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"Marinov M. B. Nikolov D. N. Ganev B. T. &Djamiykov T. S.(2020).Smart multisensor node for remote elevator condition monitoring.2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA) Bourgas Bulgaria(pp.1\u20134).https:\/\/doi.org\/10.1109\/SIELA49118.2020.9167049","DOI":"10.1109\/SIELA49118.2020.9167049"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2106598119"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9152990"},{"key":"e_1_2_10_27_1","doi-asserted-by":"crossref","unstructured":"Mishra K. M. &Huhtala K. J.(2019b).Fault detection of elevator systems using multilayer perceptron neural network.IEEE International Conference on Emerging Technologies and Factory Automation Zaragoza Spain (pp.904\u2013909).https:\/\/doi.org\/10.1109\/ETFA.2019.8869230","DOI":"10.1109\/ETFA.2019.8869230"},{"key":"e_1_2_10_28_1","unstructured":"Nguyen H. D. Tran K. P. Zeng X. Koehl L. &Tartare G.(2019).Wearable sensor data based human activity recognition using machine learning: A new approach. ArXiv Preprint ArXiv:1905.03809.https:\/\/arxiv.org\/abs\/1905.03809"},{"key":"e_1_2_10_29_1","doi-asserted-by":"crossref","unstructured":"Nikolov D. N. Marinov M. B. Ganev B. T. &Djamijkov T. S.(2020).Nonintrusive measurement of elevator velocity based on inertial and barometric sensors in autonomous node.2020 43rd International Spring Seminar on Electronics Technology (ISSE) Demanovska Valley Slovakia(pp.1\u20135).https:\/\/doi.org\/10.1109\/ISSE49702.2020.9121077","DOI":"10.1109\/ISSE49702.2020.9121077"},{"key":"e_1_2_10_30_1","unstructured":"Association of German Engineers. (2007).VDI guideline Lift Energy efficiency.http:\/\/info.wsisiz.edu.pl\/~roksela\/dzwigi\/Energia\/Energy_VDI_ENG.PDF"},{"key":"e_1_2_10_31_1","unstructured":"Olaizola J. Izagirre U. Serradilla O. Zugasti E. Mendicute M. &Aizpurua Unanue J. I.(2025).Elevator class dataset [Data set].eBiltegia. Mondragon Unibertsitatea.https:\/\/doi.org\/10.48764\/DWDV\u2010GZ94"},{"key":"e_1_2_10_32_1","unstructured":"Otis. (2024).Otis earnings call forward\u2010looking statements q32024. Otis.https:\/\/otisinvestors.com\/events\u2010and\u2010presentations\/default.aspx"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.13338"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.13301"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2613683"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2060723"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2719630"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12246"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/86.547939"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12447"},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2015.2491264"}],"container-title":["Computer-Aided Civil and Infrastructure Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/mice.13479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:59:57Z","timestamp":1767787197000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/mice.13479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":41,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1111\/mice.13479"],"URL":"https:\/\/doi.org\/10.1111\/mice.13479","archive":["Portico"],"relation":{},"ISSN":["1093-9687","1467-8667"],"issn-type":[{"value":"1093-9687","type":"print"},{"value":"1467-8667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]},"assertion":[{"value":"2024-07-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}