Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection-Reference-Cited by-同舟云学术

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Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection

Published:2018-04 Issue: Volume:64 Page:62-70
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Liu Yue,Ge Zhiqiang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference30 articles.

1. Data mining and analytics in the process industry: the role of machine learning;Ge;IEEE Access,2017

2. Review of recent research on data-based process monitoring;Ge;Ind. Eng. Chem. Res.,2013

3. Review on data-driven modeling and monitoring for plant-wide industrial processes;Ge;Chemom. Intell. Lab. Syst.,2017

4. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis;Choi;Comp. Chem. Eng.,2004

5. Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference;Jiang;Chemom. Intell. Lab. Syst.,2014

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