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

Weijun WANG; Yun WANG; Jun WANG; Xinyun FANG; Yuchen HE

doi:10.1631/FITEE.2200053

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

Regular Papers | Updated：2022-12-16

- Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification
  Enhanced Publication
- 集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 12, Pages: 1814-1827(2022)
- Affiliations：
  
  1.Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China
  2.Mechanical and Electrical Engineering Department, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311231, China
  3.Suzhou Institute of Metrology, Suzhou 215004, China
- Author bio：
  
  E-mail: S20010811027@cjlu.edu.cn;
  wy@zjtongji.edu.cn;
  blackknight@cjlu.edu.cn;
  fangxy@szjl.com.cn;
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2200053
  CLC：
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Weijun WANG, Yun WANG, Jun WANG, et al. Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification. [J]. Frontiers of Information Technology & Electronic Engineering 23(12):1814-1827(2022)
DOI：

Weijun WANG, Yun WANG, Jun WANG, et al. Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification. [J]. Frontiers of Information Technology & Electronic Engineering 23(12):1814-1827(2022) DOI： 10.1631/FITEE.2200053.

摘要

Abstract

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.

关键词

Keywords

Semi-supervisedActive learningEnsemble learningMixture discriminant analysisFault classification

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