Incorporate active learning to semi-supervised industrial fault classification

The performance of Fisher discriminant analysis (FDA) method is highly depended on the labeled data. While obtaining the true labels of the industrial data is often time-consuming and expensive in practice. Although there are some researches on semi-supervised methods based on the FDA to solve this problem, they will fail when the labeled data are not satisfied with some special conditions. To improve the methods' applicability, this paper proposes an active learning based semi-supervised FDA model for industrial fault classification. This method bridges labeling important unlabeled data of active learning and learning from unlabeled data of semi-supervised learning. The performance has been greatly improved with the information from the new labeled data, and the amount of new labeled data has been reduced by additional unlabeled data. The experimental analysis on the two dimensional data sets indicates that active learning and semi-supervised learning are complementary for each other. Finally, the experiments carried out on the UCI benchmarks and Tennessee Eastman process (TEP) prove the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.