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CLC number: TP277

On-line Access: 2022-12-14

Received: 2022-02-13

Revision Accepted: 2022-12-17

Crosschecked: 2022-05-09

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Citations:  Bibtex RefMan EndNote GB/T7714


Yun Wang


Yuchen He


Weijun WANG




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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.12 P.1814-1827


Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

Author(s):  Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Affiliation(s):  Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China; more

Corresponding email(s):   S20010811027@cjlu.edu.cn, wy@zjtongji.edu.cn, blackknight@cjlu.edu.cn, fangxy@szjl.com.cn, yche@cjlu.edu.cn

Key Words:  Semi-supervised, Active learning, Ensemble learning, Mixture discriminant analysis, Fault classification

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE. Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1814-1827.

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publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification
%A Weijun WANG
%A Xinyun FANG
%A Yuchen HE
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%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200053

T1 - Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification
A1 - Weijun WANG
A1 - Yun WANG
A1 - Jun WANG
A1 - Xinyun FANG
A1 - Yuchen HE
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
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SP - 1814
EP - 1827
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200053

As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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