Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis-Reference-Cited by-同舟云学术

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Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis

Published:2020-01-01 Issue:1 Volume:8 Page:350-358
ISSN:2164-2583
Container-title:Systems Science & Control Engineering
language:en
Short-container-title:Systems Science & Control Engineering

Author:

Miao Chen¹,Lv Zhaomin²

Affiliation:

1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People's Republic of China

2. School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, People's Republic of China

Funder

Shanghai University of Engineering Sciences

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Control and Optimization,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2020.1768173

Reference24 articles.

1. The application of principal component analysis and kernel density estimation to enhance process monitoring

2. Fault Detection and Diagnosis in Industrial Systems

3. Fault detection and identification of nonlinear processes based on kernel PCA

4. Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

5. Nonlinear principal component analysis—Based on principal curves and neural networks

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1. Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes;Engineering Applications of Artificial Intelligence;2024-10

2. An Analytical Method for Fault Detecting, Classifying, and Section Locating Based on Principal Component Analysis in Distribution Network;2024 28th International Electrical Power Distribution Conference (EPDC);2024-04-23

3. Shannon entropy Morlet wavelet Transform (SEMWT) and Kernel Weight Convolutional Neural Network (KWCNN) classifier for arrhythmia in electrocardiogram recordings;Biomedical Signal Processing and Control;2022-09

4. One step forward for smart chemical process fault detection and diagnosis;Computers & Chemical Engineering;2022-08

5. Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes;IEEE Access;2022

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