Research on Statistical Modeling of Process Data via Wavelet Domain Hidden Markov Model

Author

Zhou, Shaoyuan ; Zhu, Xuemei

Author_Institution

Zhejiang Meas. & Test Inst. for Quality & Technique Supervision, Hangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5833

Lastpage

5837

Abstract

A wavelet and hidden Markov model (HMM) based approach is introduced to build the statistical model of process data. Wavelet transform provides a compact, information-rich expression of process data through a set of coefficients that carry localized transient information of process operating condition. The non-Gaussian properties of process data are characterized by a mixture Gaussian distribution. And the serial correlations in the data are described by the state transition of hidden Markov model. Case studies from CSTR illustrate that the inherent characteristics of process data can be accurately modeled by wavelet and HMM

Keywords

Gaussian distribution; data handling; hidden Markov models; statistical analysis; wavelet transforms; data serial correlations; mixture Gaussian distribution; process data; statistical modeling; wavelet domain hidden Markov model; wavelet transform; Automatic testing; Automation; Continuous-stirred tank reactor; Data engineering; Educational institutions; Electric variables measurement; Gaussian distribution; Hidden Markov models; Wavelet domain; Wavelet transforms; CSTR; hidden Markov model; statistical model; wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714195

Filename

1714195