Enhanced multicorrelation block process monitoring and abnormity root cause analysis for distributed industrial process: A visual data-driven approach-Reference-Cited by-同舟云学术

Enhanced multicorrelation block process monitoring and abnormity root cause analysis for distributed industrial process: A visual data-driven approach

Published:2022-10 Issue: Volume:118 Page:1-15
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zhu Qun-Xiong,Wang Xin-Wei,Li Kun,Xu Yuan,He Yan-Lin

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference32 articles.

1. Novel discriminant locality preserving projection integrated with Monte Carlo sampling for fault diagnosis;He;IEEE Trans. Reliab.,2021

2. A data-driven industrial alarm decision method via evidence reasoning rule;Weng;J. Process Control,2021

3. Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder;He;IEEE Trans. Instrum. Meas.,2021

4. Improved locality preserving projections based on heat-kernel and cosine weights for fault classification in complex industrial processes;Zhang;IEEE Trans. Reliab.,2022

5. Innovative data regression incorporating deterministic knowledge for soft sensing in the process industry;Copertaro;J. Process Control,2019

Cited by 11 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. Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis;Journal of Process Control;2024-10

2. Knowledge-data-driven process monitoring based on temporal knowledge graphs and supervised contrastive learning for complex industrial processes;Journal of Process Control;2024-09

3. Manifold embedding stationary subspace analysis for nonstationary process monitoring with industrial applications;Journal of Process Control;2024-08

4. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey;Processes;2024-01-24

5. Variational Discriminative Stacked Auto-Encoder: Feature Representation Using a Prelearned Discriminator, and Its Application to Industrial Process Monitoring;IEEE Transactions on Neural Networks and Learning Systems;2024