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

Content Provider	IEEE Xplore Digital Library
Author	Jinlin Zhu Zhiqiang Ge Zhihuan Song
Copyright Year	1982
Abstract	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.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	3814
Ending Page	3821
Page Count	8
File Size	723438
File Format	PDF
ISSN	02780046
Volume Number	62
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Robustness Data models Monitoring Probabilistic logic Bayes methods Computational modeling Robust sequential data modeling Robust probabilistic principal component analyzers Mixture model Hidden Markov model Expectation maximization Outliers robust sequential data modeling Expectation–maximization (EM) hidden Markov model (HMM) mixture model outliers robust probabilistic principal component analyzers
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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