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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

 ORCID:

Yun Wang

https://orcid.org/0000-0002-7512-0168

Yuchen He

https://orcid.org/0000-0002-0528-2778

Weijun WANG

https://orcid.org/0000-0003-3655-4569

Jun WANG

https://orcid.org/0000-0002-2742-3041

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

http://doi.org/10.1631/FITEE.2200053


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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year="2022",
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doi="10.1631/FITEE.2200053"
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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.

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

王伟俊1,王云2,王君1,方信昀3,何雨辰1
1中国计量大学浙江省智能制造质量大数据溯源与应用重点实验室,中国杭州市,310018
2浙江同济科技职业学院机电工程学院,中国杭州市,311231
3苏州市计量测试院,中国苏州市,215004
摘要:故障分类作为过程监控中不可缺少的部分,其性能高度依赖于过程知识的充分性。然而,由于采样条件有限及实验室分析昂贵,数据标签总是难以获取,这可能导致分类性能下降。为了解决这个难题,本文提出一种新的半监督故障分类方法,其中每个未标记样本相对于特定标记数据集的价值采用增强的主动学习来评估。具有高价值的未标记样本将作为训练数据集的补充信息。此外,引入了几个合理的指标和准则大大降低了人工标注的干扰。最后,通过数值例子和田纳西伊士曼过程(TEP)评估了该方法的故障分类有效性。

关键词:半监督;主动学习;集成学习;混合判别分析;故障分类

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

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