{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T18:07:03Z","timestamp":1745863623227,"version":"3.37.3"},"reference-count":22,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100013290","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2019YFB2102701"],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51778372","51578336"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advances in Engineering Software"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1016\/j.advengsoft.2020.102901","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T09:38:54Z","timestamp":1598348334000},"page":"102901","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":31,"special_numbering":"C","title":["Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis"],"prefix":"10.1016","volume":"149","author":[{"given":"Linchao","family":"Li","sequence":"first","affiliation":[]},{"given":"Hanlin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haijun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chaodong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.advengsoft.2020.102901_bib0001","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.eng.2018.11.027","article-title":"The state of the art of data science and engineering in structural health monitoring","volume":"5","author":"Bao","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0002","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.aei.2018.11.006","article-title":"A new distributed time series evolution prediction model for dam deformation based on constituent elements","volume":"39","author":"Li","year":"2019","journal-title":"Adv Eng Inf"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.advengsoft.2014.02.005","article-title":"Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy","volume":"73","author":"Smarsly","year":"2014","journal-title":"Adv Eng Software"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0004","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.advengsoft.2019.03.003","article-title":"Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms","volume":"131","author":"Kang","year":"2019","journal-title":"Adv Eng Software"},{"issue":"6","key":"10.1016\/j.advengsoft.2020.102901_bib0005","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1177\/1475921717745719","article-title":"A novel distribution regression approach for data loss compensation in structural health monitoring","volume":"17","author":"Chen","year":"2018","journal-title":"Struct Health Monit"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0022","doi-asserted-by":"crossref","DOI":"10.1177\/1475921720932813","article-title":"A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems","author":"Li","year":"2020","journal-title":"Struct Health Monit"},{"issue":"23","key":"10.1016\/j.advengsoft.2020.102901_bib0006","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1016\/j.jsv.2010.05.016","article-title":"Reconstruction of dynamic displacement and velocity from measured accelerations using the variational statement of an inverse problem","volume":"329","author":"Hong","year":"2010","journal-title":"J Sound Vib"},{"issue":"2","key":"10.1016\/j.advengsoft.2020.102901_bib0007","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/1475921713513973","article-title":"Damage assessment with state\u2013space embedding strategy and singular value decomposition under stochastic excitation","volume":"13","author":"Liu","year":"2014","journal-title":"Struct Health Monit"},{"issue":"4","key":"10.1016\/j.advengsoft.2020.102901_bib0008","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1177\/1475921718794953","article-title":"Bayesian multi-task learning methodology for reconstruction of structural health monitoring data","volume":"18","author":"Wan","year":"2019","journal-title":"Struct Health Monit"},{"issue":"3\u20135","key":"10.1016\/j.advengsoft.2020.102901_bib0009","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.jsv.2006.10.021","article-title":"Reconstruction of a distributed force applied on a thin cylindrical shell by an inverse method and spatial filtering","volume":"301","author":"Djamaa","year":"2007","journal-title":"J Sound Vib"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0010","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ymssp.2011.12.010","article-title":"Structural response reconstruction based on empirical mode decomposition in time domain","volume":"28","author":"He","year":"2012","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0011","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1177\/1475921718788703","article-title":"Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach","volume":"18.4","author":"Chen","year":"2019","journal-title":"Struct Health Monit"},{"key":"10.1016\/j.advengsoft.2020.102901_bib0012","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.advengsoft.2018.10.005","article-title":"A novel principal components analysis (PCA) method for energy absorbing structural design enhanced by data mining","volume":"127","author":"Du","year":"2019","journal-title":"Adv Eng Software"},{"issue":"4","key":"10.1016\/j.advengsoft.2020.102901_bib0013","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.aei.2015.10.002","article-title":"Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data","volume":"29","author":"Jung","year":"2015","journal-title":"Adv Eng Inf"},{"issue":"1\u20133","key":"10.1016\/j.advengsoft.2020.102901_bib0014","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom Intell Lab Syst"},{"issue":"1\u20132","key":"10.1016\/j.advengsoft.2020.102901_bib0015","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.jsv.2005.03.016","article-title":"Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks","volume":"290","author":"Ni","year":"2006","journal-title":"J Sound Vib"},{"issue":"1","key":"10.1016\/j.advengsoft.2020.102901_bib0016","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.aei.2007.02.002","article-title":"Model-free data interpretation for continuous monitoring of complex structures","volume":"22","author":"Posenato","year":"2008","journal-title":"Adv Eng Inf"},{"issue":"5","key":"10.1016\/j.advengsoft.2020.102901_bib0017","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1177\/1475921710388972","article-title":"Q-statistic and T2-statistic PCA-based measures for damage assessment in structures","volume":"10","author":"Mujica","year":"2011","journal-title":"Struct Health Monit"},{"issue":"10","key":"10.1016\/j.advengsoft.2020.102901_bib0018","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1002\/stc.1540","article-title":"Damage classification in structural health monitoring using principal component analysis and self\u2010organizing maps","volume":"20","author":"Tibaduiza","year":"2013","journal-title":"Struct Control Health Monit"},{"issue":"1\u20132","key":"10.1016\/j.advengsoft.2020.102901_bib0019","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.ymssp.2013.05.020","article-title":"A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring","volume":"41","author":"Tibaduiza","year":"2013","journal-title":"Mech Syst Signal Process"},{"issue":"Jul","key":"10.1016\/j.advengsoft.2020.102901_bib0020","first-page":"1957","article-title":"Practical approaches to principal component analysis in the presence of missing values","volume":"11","author":"Ilin","year":"2010","journal-title":"J Mach Learn Res"},{"issue":"2","key":"10.1016\/j.advengsoft.2020.102901_bib0021","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1049\/iet-its.2017.0273","article-title":"Robust and flexible strategy for missing data imputation in intelligent transportation system","volume":"12","author":"Li","year":"2017","journal-title":"IET Intel Transport Syst"}],"container-title":["Advances in Engineering Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0965997820301848?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0965997820301848?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T22:16:21Z","timestamp":1602886581000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0965997820301848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":22,"alternative-id":["S0965997820301848"],"URL":"https:\/\/doi.org\/10.1016\/j.advengsoft.2020.102901","relation":{},"ISSN":["0965-9978"],"issn-type":[{"type":"print","value":"0965-9978"}],"subject":[],"published":{"date-parts":[[2020,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis","name":"articletitle","label":"Article Title"},{"value":"Advances in Engineering Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.advengsoft.2020.102901","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102901"}}