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However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance. <\/jats:p>","DOI":"10.1177\/1550147719857583","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T06:44:26Z","timestamp":1560926666000},"page":"155014771985758","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Process monitoring based on distributed principal component analysis with angle-relevant variable selection"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5399-5297","authenticated-orcid":false,"given":"Chen","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Institute of Automation, Jiangnan University, Wuxi, 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