Partial kernel PCA-based GLRT for fault diagnosis of nonlinear processes-Reference-Cited by-同舟云学术

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Partial kernel PCA-based GLRT for fault diagnosis of nonlinear processes

Published:2020-04-30 Issue:4 Volume:38 Page:4829-4843
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Fezai Radhia¹,Mansouri Majdi²,Abodayeh Kamaleldin³,Nounou Hazem²,Nounou Mohamed⁴,Messaoud Hassani¹

Affiliation:

1. Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, Tunisia

2. Electrical and Computer Engineering Program, Texas A&M University at Qatar, Qatar

3. Department of Mathematical Sciences, Prince Sultan University, Riyadh, Saudi Arabia

4. Chemical Engineering Program, Texas A&M University at Qatar, Qatar

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference23 articles.

1. Enhanced data validation strategy of air quality monitoring network;Harkat;Environmental Research,2018

2. On the fault diagnosis problem for non-linear systems: A fuzzy sliding-mode observer approach 1;Castillo-Toledo;Journal of Intelligent & Fuzzy Systems,2009

3. Fault diagnoses of steam turbine using the exponential similarity measure of neutrosophic numbers;Ye;Journal of Intelligent & Fuzzy Systems,2016

4. Review of recent research on databased process monitoring;Ge;Industrial & Engineering Chemistry Research,2013

5. Fault detection based on augmented kernel mahalanobis distance for nonlinear dynamic processes;Shang;Computers & Chemical Engineering,2018

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1. KPI-related monitoring approach for powertrain system in hybrid electric vehicles;Energy Reports;2024-06

2. Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator;IET Control Theory & Applications;2022-10-14

3. Autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis;Journal of Process Control;2021-05

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