Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods
Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, an...
Main Authors: | Guo-Zhu Wang, Jing Li, Yong-Tao Hu, Yuan Li, Zhi-Yong Du |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-02-01
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Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/4/929 |
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