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Bayesian online change point detection method for process monitoring
Pan YJ(潘怡君)1,2,3; Zheng ZY(郑泽宇)1,2,3
Department数字工厂研究室
Conference Name32nd Chinese Control and Decision Conference, CCDC 2020
Conference DateAugust 22-24, 2020
Conference PlaceHefei, China
Author of SourceIEEE Control Systems Society (CSS) ; Northeastern University ; State Key Laboratory of Synthetical Automation for Process Industries ; Technical Committee on Control Theory, Chinese Association of Automation
Source PublicationProceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
PublisherIEEE
Publication PlaceNew York
2020
Pages3389-3393
Indexed ByEI
EI Accession number20204009254912
Contribution Rank1
ISBN978-1-7281-5854-9
Keywordfault detection Bayesian change point detection industrial process exponential family
AbstractAiming at the problem of a large amount of unlabeled observations collected in the industrial processes, an unsupervised Bayesian online change point detection method is adopted for fault detection. Firstly, a prior probability of fault occurrence is set based on the significance level. Secondly, the predictive distribution is calculated using the exponential family likelihoods as a new observation arrives. Finally, based on the observed data, a recursive message-passing algorithm is applied for calculating the fault occurrence probability at the current sampling point. The power of the Bayesian method for fault detection is tested in a numerical simulation and the Tennessee-Eastman (TE) process.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27697
Collection数字工厂研究室
Corresponding AuthorZheng ZY(郑泽宇)
Affiliation1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
Recommended Citation
GB/T 7714
Pan YJ,Zheng ZY. Bayesian online change point detection method for process monitoring[C]//IEEE Control Systems Society (CSS), Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Technical Committee on Control Theory, Chinese Association of Automation. New York:IEEE,2020:3389-3393.
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