Online reduced gaussian process regression based generalized likelihood ratio test for fault detection-Reference-Cited by-同舟云学术

Online reduced gaussian process regression based generalized likelihood ratio test for fault detection

Published:2020-01 Issue: Volume:85 Page:30-40
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

R. Fezai,M. Mansouri,K. Abodayeh,H. Nounou,M. Nounou

Funder

Qatar National Research Fund

Publisher

Elsevier BV

Subject

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

Reference48 articles.

1. Multiscale kernel pls-based exponentially weighted-glrt and its application to fault detection;Mansouri;IEEE Trans. Emerg. Top. Comput. Intell.,2017

2. Monitoring of wastewater treatment plants using improved univariate statistical technique;Baklouti;Process Saf. Environ. Prot.,2018

3. Wavelet optimized EWMA for fault detection and application to photovoltaic systems;Mansouri;Sol. Energy,2018

4. Enhanced data validation strategy of air quality monitoring network;Harkat;Environ. Res.,2018

5. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results;Frank;Automatica,1990

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning for control of (bio)chemical manufacturing systems;Artificial Intelligence in Manufacturing;2024

2. Abnormal Condition Detection Method of Industrial Processes Based on the Cascaded Bagging-PCA and CNN Classification Network;IEEE Transactions on Industrial Informatics;2023-11

3. Multi-scale adaptive multivariate state estimation fault detection enhancement for time-varying industrial system based on multi-output Gaussian process autoregression;Computers & Industrial Engineering;2023-09

4. Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble;Chemical Engineering Science;2023-09

5. Faults prediction and monitoring of complex processes using an ensemble of machine learning regression models: application to the Tennessee Eastman Process;2023-06-09