文章基本信息

标题：Process Monitoring with Sparse Bayesian Model for Industrial Methanol Distillation
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作者：Lin Luo ; Lei Xie ; Hongye Su 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：424-430
DOI：10.1016/j.ifacol.2020.12.210
语种：English
出版社：Elsevier
摘要：AbstractFollowing the intuition that not all latent variables in probabilistic principal component analysis method shifts simultaneously, this paper proposes a spike-and-slab regularization technique for nonlinear fault detection and isolation. Different from the existing probabilistic latent variable models, a spike-and-slab prior is introduced to downweight the irrelevant information of latent variables for the discriminative model. The resulting latent subspace supported by regularization parameters is not only sensitive to the informative variables, but it also eliminates the influence of the non-informative ones. The feasibility and efficiency of the proposed approach will be tested on an industrial methanol distillation dataset. Moreover, the performance will be compared with conventional probabilistic latent variables methods.
关键词：KeywordsProcess monitoringfault detectionisolationBayesian variable selectionspike-and-slab priorindustrial methanol distillation