HMM-Driven Robust Probabilistic Principal Component Analyzer for Dynamic Process Fault Classification

被引:61
作者
Zhu, Jinlin [1 ]
Ge, Zhiqiang [1 ]
Song, Zhihuan [1 ]
机构
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Dept Control Sci & Engn, Hangzhou 310027, Peoples R China
基金
中国国家自然科学基金;
关键词
Expectation-maximization (EM); hidden Markov model (HMM); mixture model; outliers; robust probabilistic principal component analyzers; robust sequential data modeling; BAYESIAN-INFERENCE; MIXTURES; MODEL;
D O I
10.1109/TIE.2015.2396877
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a novel hidden Markov model (HMM)-driven robust latent variable model (LVM) is proposed for fault classification in dynamic industrial processes. A robust probabilistic model with Student's t mixture output is designed for tolerating outliers. Based on the robust LVM, the probabilistic structure is further developed into a classifier form so as to incorporate various types of process information during model acquisition. After that, the robust probabilistic classifier is extended within the HMM framework so as to characterize the time-domain stochastic uncertainties. The model parameters are derived through the expectation-maximization algorithm. For performance validation, the developed model is tested on the Tennessee Eastman benchmark process.
引用
收藏
页码:3814 / 3821
页数:8
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