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

2022, Volume 23, Issue 12

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Frontiers of Information Technology & Electronic Engineering >> 2022, Volume 23, Issue 12 doi: 10.1631/FITEE.2200053

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

Affiliation(s): Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China; Mechanical and Electrical Engineering Department, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311231, China; Suzhou Institute of Metrology, Suzhou 215004, China; less

Received: 2022-02-13 Accepted: 2022-12-14 Available online: 2022-12-14

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Abstract

As an indispensable part of process monitoring, the performance of 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 strategy is performed in which enhanced 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 effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords

Semi-supervised ; Active learning ; Ensemble learning ; Mixture discriminant analysis ; Fault classification

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