{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:28:46Z","timestamp":1766269726055,"version":"3.37.3"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,17]]},"DOI":"10.1109\/safeprocess52771.2021.9693604","type":"proceedings-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T20:50:05Z","timestamp":1643748605000},"page":"1-6","source":"Crossref","is-referenced-by-count":2,"title":["Multimode Processes Monitoring based on Slow Feature Analysis with Personalized Modeling"],"prefix":"10.1109","author":[{"given":"Xin","family":"Ma","sequence":"first","affiliation":[]},{"given":"Shaoxu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Rongmin","family":"Chai","sequence":"additional","affiliation":[]},{"given":"Qiankun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haixin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Youqing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Recursive correlative statistical analysis method with sliding windows for incipient fault detection","author":"qin","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.0c02256"},{"key":"ref12","article-title":"Artificial Neural Correlation Analysis for Performance-Indicator-Related Nonlinear Process Monitoring","author":"chen","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheatmasstransfer.2008.05.016"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.019"},{"key":"ref15","article-title":"Degradation state partition and compound fault diagnosis of rolling bearing based on personalized multi-label learning","author":"ma","year":"0","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3004681"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2015.12.004"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2015.2497545"},{"key":"ref19","article-title":"Hierarchical compound fault diagnosis of rotating machinery based on multi-label learning","author":"ma","year":"2021","journal-title":"Control and Decision"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1021\/ie302069q","article-title":"Review of recent research on data-based process monitoring","volume":"52","author":"ge","year":"2013","journal-title":"Industrial % Rngineering Chemistry Research"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","article-title":"From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis","volume":"9","author":"dai","year":"2013","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2818538"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2872541"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2972472"},{"key":"ref7","article-title":"PCA-based Ensemble Detector for Incipient Faults in Dynamic Processes","author":"liu","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.02.028"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/cjce.23249"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2020.104692"}],"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\/09693604.pdf?arnumber=9693604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:58:56Z","timestamp":1652201936000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693604\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,17]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/safeprocess52771.2021.9693604","relation":{},"subject":[],"published":{"date-parts":[[2021,12,17]]}}}