{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:15:30Z","timestamp":1740100530350,"version":"3.37.3"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,17]]},"DOI":"10.1109\/safeprocess52771.2021.9693664","type":"proceedings-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T20:50:05Z","timestamp":1643748605000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["Supervised Dynamic Latent Variable Models for Fault Identification in Dynamic Processes"],"prefix":"10.1109","author":[{"given":"Qiqi","family":"Niu","sequence":"first","affiliation":[{"name":"Zhejiang University of Science & Technology,School of Automation and Electrical Engineering,Hangzhou,Zhejiang,China"}]},{"given":"Chengkai","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang University of Science & Technology,School of Automation and Electrical Engineering,Hangzhou,Zhejiang,China"}]},{"given":"Yuting","family":"Lyu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Science & Technology,School of Automation and Electrical Engineering,Hangzhou,Zhejiang,China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Process Equipment and Control Engineering Zhejiang University of Technology,Hangzhou,Zhejiang,China"}]},{"given":"Le","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University of Science & Technology,School of Automation and Electrical Engineering,Hangzhou,Zhejiang,China"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic principal component analysis","volume":"61","author":"tipping","year":"1999","journal-title":"Journal of the Royal Statistical Society Series B (Statistical Methodology)"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"18280","DOI":"10.1021\/acs.iecr.9b03069","article-title":"Accelerated Kernel Canonical Correlation Analysis with Fault Relevance for Nonlinear Process Fault Isolation","volume":"58","author":"yu","year":"2019","journal-title":"Industrial & Engineering Chemistry Research"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"3763","DOI":"10.1002\/aic.13776","article-title":"Data-based linear Gaussian state-space model for dynamic process monitoring","volume":"58","author":"wen","year":"2012","journal-title":"AIChE Journal"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/TCST.2016.2550426","article-title":"Autoregressive dynamic latent variable models for process monitoring","volume":"25","author":"zhou","year":"2017","journal-title":"IEEE Transactions on Control Systems Technology"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0169-7439(95)00076-3","article-title":"Disturbance detection and isolation by dynamic principal component analysis","volume":"30","author":"ku","year":"1995","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(00)00058-7"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.chemolab.2003.10.011","article-title":"Sensor fault identification based on time-lagged PCA in dynamic processes","volume":"70","author":"lee","year":"2004","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2014.12.001"},{"key":"ref18","first-page":"775","article-title":"Contribution plots: a missing link in multivariate quality control","volume":"8","author":"miller","year":"1998","journal-title":"Applied Mathematics and Computer Science"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"9779","DOI":"10.1021\/acs.iecr.7b05189","article-title":"Reconstruction-based multivariate process fault isolation using bayesian lasso","volume":"57","author":"yan","year":"2018","journal-title":"Industrial & Engineering Chemistry Research"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2015.2481318"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/cem.800"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690400509"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2009.07.005"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jtice.2017.08.008","article-title":"Parallel projection to latent structures for quality-relevant process monitoring","volume":"80","author":"zheng","year":"2017","journal-title":"Journal of the Taiwan Institute of Chemical Engineers"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: An overview with application to learning methods","volume":"16","author":"hardoon","year":"2004","journal-title":"Neural Computation"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2756872"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3053128"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TII.2009.2032654","article-title":"Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations","volume":"6","author":"odiowei","year":"2009","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/S0098-1354(97)00277-9"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1109\/TSMC.2021.3051054","article-title":"Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis","author":"yu","year":"2021","journal-title":"IEEE Transactions on Systems Man and Cybernetics Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2009.02.027"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"4076","DOI":"10.1109\/TII.2018.2889750","article-title":"Multirate factor analysis models for fault detection in multirate processes","volume":"15","author":"zhou","year":"2018","journal-title":"IEEE Transactions on Industrial Informatics"}],"event":{"name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","start":{"date-parts":[[2021,12,17]]},"location":"Chengdu, China","end":{"date-parts":[[2021,12,18]]}},"container-title":["2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9693491\/9693537\/09693664.pdf?arnumber=9693664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:22:39Z","timestamp":1654546959000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693664\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,17]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/safeprocess52771.2021.9693664","relation":{},"subject":[],"published":{"date-parts":[[2021,12,17]]}}}