文章基本信息

标题：A variational Bayesian robust linear dynamic system approach for dynamic process modelling and fault detection
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作者：Jinlin Zhu Zhiqiang Ge ; Zhihuan Song
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：21
页码：535-540
DOI：10.1016/j.ifacol.2015.09.581
语种：English
出版社：Elsevier
摘要：In this work, a variational Bayesian robust linear dynamic system (VBRLDS) approach is proposed for dynamic process modeling and monitoring. Traditional linear dynamic system (LDS) constructed with Kalman filter is designed by Gaussian assumption which can be easily violated in outlier contaminated modeling situations. To deal with this issue, the conventional Gaussian-based Kalman filter is modified with heavy tailed Student's t-distribution so as to deal with modeling outliers. Then, a variational Bayesian expectation maximization (VBEM) algorithm is developed for learning parameters of the robust linear dynamic system. For process monitoring, traditional residual space modeling method of SPE statistics are modified. To explore the feasibility and effectiveness, our proposed method is applied into fault detection and is comparatively studied with several other methods on the Tennessee Eastman benchmark.
关键词：Robust LDSVariational BayesianKalman filterStudent's t-distributionFault detection