A MIIPCR Fault Detection Strategy for TEP

Multivariate statistical method is one of data-driven fault diagnosis methods, which is widely used in complex industrial systems to realize faults detection. And, the common methods include the principal component analysis, principal component regression, and partial least squares (PLS). Compared w...

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Main Authors: Chengcong Lv, Aihua Zhang, Zhiqiang Zhang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
TEP
Online Access:https://ieeexplore.ieee.org/document/8618606/
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spelling doaj-df1864a08bd440e484ca2e2789ebef6a2021-03-29T22:21:35ZengIEEEIEEE Access2169-35362019-01-017187491875410.1109/ACCESS.2019.28932088618606A MIIPCR Fault Detection Strategy for TEPChengcong Lv0Aihua Zhang1https://orcid.org/0000-0001-7324-9948Zhiqiang Zhang2https://orcid.org/0000-0002-5488-6244College of Engineering, Bohai University, Jinzhou, ChinaCollege of Engineering, Bohai University, Jinzhou, ChinaCollege of Engineering, Bohai University, Jinzhou, ChinaMultivariate statistical method is one of data-driven fault diagnosis methods, which is widely used in complex industrial systems to realize faults detection. And, the common methods include the principal component analysis, principal component regression, and partial least squares (PLS). Compared with the PLS, improved principal component regression (IPCR) improves the alarm performance in the quality-related and quality-independent parts. Considering the advantage of the mutual information is good for selecting quality-related variables for modeling, the mutual information is employed to improve the principal component regression (MIIPCR). Through the design and analysis of the algorithms, the MIIPCR can give a performance boost for the principal components, especially for the IPCR in fault detection of Tennessee Eastman process (TEP). Compared with the IPCR, the MIIPCR has the strong advantage to improve feedback failures five and seven of the TEP. In addition, the MIIPCR also could apply to detect the other faults and confirm the higher than the IPCR. And for the unrelated faults, the MIIPCR also has the great identification ability. Lots of simulation had been on for the TEP, the simulation results showed that it's the great effectiveness of the MIIPCR.https://ieeexplore.ieee.org/document/8618606/MIIPCRTEPfault detectionquality-relatedmutual information principal component
collection DOAJ
language English
format Article
sources DOAJ
author Chengcong Lv
Aihua Zhang
Zhiqiang Zhang
spellingShingle Chengcong Lv
Aihua Zhang
Zhiqiang Zhang
A MIIPCR Fault Detection Strategy for TEP
IEEE Access
MIIPCR
TEP
fault detection
quality-related
mutual information principal component
author_facet Chengcong Lv
Aihua Zhang
Zhiqiang Zhang
author_sort Chengcong Lv
title A MIIPCR Fault Detection Strategy for TEP
title_short A MIIPCR Fault Detection Strategy for TEP
title_full A MIIPCR Fault Detection Strategy for TEP
title_fullStr A MIIPCR Fault Detection Strategy for TEP
title_full_unstemmed A MIIPCR Fault Detection Strategy for TEP
title_sort miipcr fault detection strategy for tep
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Multivariate statistical method is one of data-driven fault diagnosis methods, which is widely used in complex industrial systems to realize faults detection. And, the common methods include the principal component analysis, principal component regression, and partial least squares (PLS). Compared with the PLS, improved principal component regression (IPCR) improves the alarm performance in the quality-related and quality-independent parts. Considering the advantage of the mutual information is good for selecting quality-related variables for modeling, the mutual information is employed to improve the principal component regression (MIIPCR). Through the design and analysis of the algorithms, the MIIPCR can give a performance boost for the principal components, especially for the IPCR in fault detection of Tennessee Eastman process (TEP). Compared with the IPCR, the MIIPCR has the strong advantage to improve feedback failures five and seven of the TEP. In addition, the MIIPCR also could apply to detect the other faults and confirm the higher than the IPCR. And for the unrelated faults, the MIIPCR also has the great identification ability. Lots of simulation had been on for the TEP, the simulation results showed that it's the great effectiveness of the MIIPCR.
topic MIIPCR
TEP
fault detection
quality-related
mutual information principal component
url https://ieeexplore.ieee.org/document/8618606/
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