Dynamic mutual information similarity based transient process identification and fault detection-Reference-Cited by-同舟云学术

Dynamic mutual information similarity based transient process identification and fault detection

Published:2018-01-27 Issue:7 Volume:96 Page:1541-1558
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can. J. Chem. Eng.

Author:

He Yuchen¹²,Zhou Le³,Ge Zhiqiang²,Song Zhihuan²

Affiliation:

1. College of Mechanical and Electrical Engineering; China JiLiang University; Hangzhou 310018 Zhejiang P. R. China

2. State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering; Zhejiang University; Hangzhou 310027 Zhejiang P. R. China

3. School of Automation and Electrical Engineering; Zhejiang University of Science and Technology; Hangzhou 310023 Zhejiang P. R. China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cjce.23102/fullpdf

Reference41 articles.

1. Improved PLS algorithms

2. Determining the number of principal components for best reconstruction

3. Orthogonal projections to latent structures (O-PLS)

4. Isolation enhanced principal component analysis

Cited by 22 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. Overview and prospect of abnormal monitoring methods for non-stationary processes;SCIENTIA SINICA Informationis;2024-08-01

2. Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes;IEEE Sensors Journal;2024-07-01

3. Hybrid modeling approach for natural gas desulfurization process: Coupling mechanism and data modeling via compact variable identification;Gas Science and Engineering;2024-03

4. Mode identification method based on model generalization and probability density difference for process data;2023 35th Chinese Control and Decision Conference (CCDC);2023-05-20

5. A Multistep Sequence-to-Sequence Model With Attention LSTM Neural Networks for Industrial Soft Sensor Application;IEEE Sensors Journal;2023-05-15