Bayesian treed gaussian process method for process monitoring

Bayesian treed gaussian process method for process monitoring

Author Wang, Hangzhou Google Scholar
Melo, Vinicius Veloso de Autor UNIFESP Google Scholar
Abstract The Bayesian treed Gaussian method is introduced in this paper to implement process monitoring based on historical data. This method can cover the disturbances in a process and discover differences among individually monitored variables before and after an abnormal situation occurs. The analysis results from the historical values of each variable help to differentiate abnormal from normal states in the process. Here, the Tennessee Eastman process is studied to show the effectiveness of this method for process monitoring.
Keywords Bayesian Treed Gaussian Process
Multivariate Statistical Process Monitoring
Nonparametric Regression
Tennessee Eastman ProcessMixture Model
Fault-Detection
Diagnosis
Language English
Sponsor NSFC [21306100]
Brazilian Government CNPq [486950/2013-1]
Brazilian Government CAPES [12180-13-0]
Grant number NSFC: 21306100
CNPq: 486950/2013-1
CAPES: 12180-13-0
Date 2016
Published in 26th European Symposium On Computer Aided Process Engineering (ESCAPE), Pt B. Amsterdam, v. 38B, p. 1773-1778, 2016.
ISSN 1570-7946 (Sherpa/Romeo, impact factor)
Publisher Univ Sao Paulo, Escola De Enfermagem De Ribeirao Preto
Extent 1773-1778
Origin http://dx.doi.org/10.1016/B978-0-444-63428-3.50300-3
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000406968900093
URI http://repositorio.unifesp.br/handle/11600/49222

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