Spectral radius-based interval principal component analysis (SR-IPCA) for fault detection in industrial processes with imprecise data-Reference-Cited by-同舟云学术

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Spectral radius-based interval principal component analysis (SR-IPCA) for fault detection in industrial processes with imprecise data

Published:2022-06 Issue: Volume:114 Page:105-119
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zhang Shumei^ORCID,Wang Sijia

Funder

Natural Science Foundation of Tianjin City

State Key Laboratory of Industrial Control Technology

National Natural Science Foundation of China

Zhejiang University

Publisher

Elsevier BV

Subject

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

Reference37 articles.

1. Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis;Zheng;J. Process Control,2019

2. A robust supervised subspace learning approach for output-relevant prediction and detection against outliers;Li;J. Process Control,2021

3. Total variable decomposition based on sparse cointegration analysis for distributed monitoring of nonstationary industrial processes;Zhao;IEEE Trans. Control Syst. Trans.,2020

4. Comprehensive process decomposition for closed-loop process monitoring with quality-relevant slow feature analysis;Qin;J. Process Control,2019

5. Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes;Jiang;Ind. Eng. Chem. Res.,2019

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4. Nonlinear Dynamic System Based on SAE-LDS Model for Fault Diagnosis;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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