{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:52:09Z","timestamp":1761562329842,"version":"3.37.3"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573050","61473025"],"award-info":[{"award-number":["61573050","61473025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open-Project Grant"},{"DOI":"10.13039\/501100004184","name":"State Key Laboratory of Synthetical Automation for Process Industry, Northeastern University","doi-asserted-by":"publisher","award":["PAL-N201702"],"award-info":[{"award-number":["PAL-N201702"]}],"id":[{"id":"10.13039\/501100004184","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2795535","type":"journal-article","created":{"date-parts":[[2018,1,19]],"date-time":"2018-01-19T14:19:23Z","timestamp":1516371563000},"page":"6360-6369","source":"Crossref","is-referenced-by-count":23,"title":["Process Monitoring Based on Multivariate Causality Analysis and Probability Inference"],"prefix":"10.1109","volume":"6","author":[{"given":"Xiaolu","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6847-8452","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jinglin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"Distinguishing Between Cause and Effect","year":"2008","author":"mooij","key":"ref39"},{"key":"ref38","first-page":"269","article-title":"Bayesian updating in causal probabilistic networks by local computations","volume":"4","author":"jensen","year":"1990","journal-title":"Computational Statistics Quaterly"},{"key":"ref33","first-page":"15","author":"korb","year":"2004","journal-title":"Bayesian Artificial Intelligence"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2714579"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/prs.11658"},{"key":"ref30","first-page":"123","author":"chen","year":"2016","journal-title":"Statistics and Causality Methods for Applied Empirical Research"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2006.14"},{"article-title":"A kernel statistical test of independence","year":"0","author":"gretton","key":"ref36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(00)00026-5"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2732727"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/cjce.22921"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2011.529265"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2667683"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/cjce.22855"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.5b04023"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/aic.12760"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/aic.12392"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1002\/cjce.23002"},{"key":"ref17","first-page":"507","author":"hipel","year":"2011","journal-title":"The Graph Model for Conflict Resolution"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2579306"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.12.007"},{"key":"ref28","first-page":"1225","article-title":"DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model","volume":"12","author":"shimizu","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1021\/ie302069q"},{"key":"ref27","first-page":"2003","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"shimizu","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2718498"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2017.09.021"},{"article-title":"Identifiability of causal graphs using functional models","year":"0","author":"peters","key":"ref29"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2697210"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.5b00567"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2301773"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2640308"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.7b01721"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.81"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2747153"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2017.08.011"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2621010"},{"key":"ref24","first-page":"111","article-title":"Pairwise likelihood ratios for estimation of non-Gaussian structural equation models","volume":"14","author":"hyv\u00e4rinen","year":"2010","journal-title":"J Mach Learn Res"},{"article-title":"Causal inference by choosing graphs with most plausible Markov kernels","year":"0","author":"sun","key":"ref23"},{"key":"ref26","first-page":"157","article-title":"Distinguishing causes from effects using nonlinear acyclic causal models","volume":"6","author":"zhang","year":"2010","journal-title":"Proc JMLR Workshop Conf"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2746539"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08263608.pdf?arnumber=8263608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:38:38Z","timestamp":1641987518000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8263608\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2795535","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2018]]}}}