Manifold-Preserving Sparse Graph and Deviation Information Based Fisher Discriminant Analysis for Industrial Fault Classification Considering Label-Noise and Unobserved Faults-Reference-Cited by-同舟云学术

Manifold-Preserving Sparse Graph and Deviation Information Based Fisher Discriminant Analysis for Industrial Fault Classification Considering Label-Noise and Unobserved Faults

Published:2022-03-01 Issue:5 Volume:22 Page:4257-4267
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Liu Jun¹,Jiang Peng²^ORCID,Song Chunyue³^ORCID,Xu Huan⁴^ORCID,Hmelnov Alexei E.⁵

Affiliation:

1. School of Automation (Artificial Intelligence), “the Belt and Road” Institute for Information Technology, Hangzhou Dianzi University, Hangzhou, China

2. School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China

3. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, China

4. School of Automation (Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, China

5. Siberian Branch, Russian Academy of Sciences, Matrosov Institute for System Dynamics and Control Theory, Irkutsk, Russia

Funder

Zhejiang Provincial Natural Science Foundation of China

Leading Talents of Science and Technology Innovation in Zhejiang Provincial Ten Thousands Plan

Fundamental Research Funds for the Provincial Universities of Zhejiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9722574/09667529.pdf?arnumber=9667529

Reference31 articles.

1. Automatic load change system of cryogenic air separation process

2. A plant-wide industrial process control problem

3. SVM-tree and SVM-forest algorithms for imbalanced fault classification in industrial processes

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

5. Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification

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