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  • 标题:Dynamic CCA-Based Distributed Monitoring for Multiunit Non-Gaussian Processes
  • 本地全文:下载
  • 作者:Qingchao Jiang ; Xuefeng Yan
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:21
  • 页码:347-352
  • DOI:10.1016/j.ifacol.2018.09.444
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractMultiunit non-Gaussian dynamic processes are more popular, and monitoring of such processes becomes important. A dynamic canonical correlation analysis (DCCA)-based distributed monitoring approach for multiunit non-Gaussian dynamic processes is proposed. First, DCCA is performed between each local unit and the coupled units, which models both the auto-correlation and cross-correlation among units. Second, two fault detection residuals are generated for each local unit, through which both the process status and the type of a detected fault are identified. The proposed DCCA monitoring scheme achieves local unit monitoring by considering the variables from both the local unit and the coupled units, and therefore exhibits superiority. Applications on the Tennessee Eastman benchmark process are provided, through which the efficiency of the DCCA monitoring method is demonstrated.
  • 关键词:Keywordsdistributed process monitoringmultiunit processesnon-Gaussian dynamic process monitoringcanonical correlation analysis
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