Multirate Dynamic Process Monitoring Based on Multirate Linear Gaussian State-Space Model

Multivariate statistical process monitoring (MSPM) has been widely used in modern industries and most of traditional MSPM methods are developed using uniformly sampled measurements. However, process variables are often sampled with different rates in practical industries. On the other hand, most of...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 16; no. 4; pp. 1708 - 1719
Main Authors: Cong, Ya, Zhou, Le, Song, Zhihuan, Ge, Zhiqiang
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Benchmarks
Control charts
Data processing
Datasets
Dynamic process monitoring
expectation maximization (EM)
Expectation-maximization algorithms
Kalman filters
Monitoring
multirate linear Gaussian state-space (MLGSS) model
multirate processes monitoring
Probabilistic logic
probabilistic modeling
Process controls
Process variables
Sampling methods
State space models
State-space methods
Variables
United States > US
Tennessee
ISSN:1545-5955, 1558-3783
Online Access:Get full text
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Summary:Multivariate statistical process monitoring (MSPM) has been widely used in modern industries and most of traditional MSPM methods are developed using uniformly sampled measurements. However, process variables are often sampled with different rates in practical industries. On the other hand, most of the industries are dynamic processes in which the measurements are highly autocorrelated. Thus, it is difficult to build a dynamic process model with incomplete data sets in multirate processes. In this paper, a multirate linear Gaussian state-space model is exploited to deal with the above issues. Both the offline model training and online process monitoring schemes are developed in the present of incomplete multirate process data sets. The proposed method is validated through a numerical example and the Tennessee Eastman benchmark process.
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content type line 14
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2019.2896205