A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification m...

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Bibliographic Details
Main Authors: Xuqing Jia, Wende Tian, Chuankun Li, Xia Yang, Zhongjun Luo, Hui Wang
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Processes
Subjects:
semi-supervised learning
active learning
feature selection
ontology
chemical process
fault identification
Online Access:https://www.mdpi.com/2227-9717/8/1/105

Internet

https://www.mdpi.com/2227-9717/8/1/105

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