K-means Bayes algorithm for imbalanced fault classification and big data application. (September 2019)

• Record Type: Journal Article
• Authors: Chen, Gecheng; Liu, Yue; Ge, Zhiqiang
• Is Part Of: Journal of process control. Volume 81(2019)
• Page Start: 54
• Page End: 64
• Publication Date: 2019-09
• Subjects: Imbalanced data -- K-means -- Naive Bayes -- MapReduce -- Fault classification; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.06.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: K-means Bayes algorithm is newly proposed for imbalanced fault classification. The proposed method neither adds samples nor reduces samples, ensuring the quality of the training data. A MapReduce form of the proposed algorithm is further developed for big data application. The superiority of the developed method is tested on a benchmark process. Abstract: Fault classification is an important part of process monitoring in industrial processes. Most conventional fault classification methods are under the assumption that the amount of data in different classes are similar. However, in practice, most of the data collected from industrial process are normal data (majority) and only a few of them are fault data (minority). In other words, fault classification can be seen as an imbalanced data classification problem, which has not been considered in this area to date. In this paper, a K-means Bayes algorithm is proposed to deal with the imbalanced fault classification problem. After that, a MapReduce approach is further introduced to implement the method for fault classification in the big data case. Effectiveness of the proposed method is verified through experiments based on Tennessee Eastman (TE) benchmark process.