标题:Multimode process monitoring based on conditionally independent Bayesian learning
作者:Shang, Jun ;Chen, Maoyin ;Zhou, Donghua
作者机构:[Shang, Jun ;Chen, Maoyin ;Zhou, Donghua ] Department of Automation, TNList, Tsinghua University, Beijing; 100084, China;[Zhou, Donghua ] College of E 更多
会议名称:56th IEEE Annual Conference on Decision and Control, CDC 2017
会议日期:December 12, 2017 - December 15, 2017
来源:2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
出版年:2017
卷:2018-January
页码:2705-2710
DOI:10.1109/CDC.2017.8264052
摘要:In multimode process monitoring, both mode identification and fault detection are important for system safety and reliability. In this paper, a new data-driven multimode process monitoring method called conditionally independent Bayesian learning (CIBL) is proposed. Considering the strong assumption of conditional independence in naïve Bayes, orthogonal transformation is first applied to measured variables to improve the extent of conditional independence in different operating modes, without excessively changing the data features. Then Bayes-based mode identification is adopted in transformed data, and the Mahalanobis distance of the transformed measurement vector serves as the detection index. With this orthogonal transformation, the mode identification accuracy can be effectively improved compared with naïve Bayes. In addition, the fault detection performance of the proposed method outperforms the traditional multimode process monitoring method mixture principal component analysis.
© 2017 IEEE.
收录类别:EI
资源类型:会议论文
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