Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring-Reference-Cited by-同舟云学术

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Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Published:2022-12-27 Issue: Volume: Page:
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Yu Feng¹²^ORCID,Liu Jianchang¹²^ORCID,Liu Dongming¹²^ORCID,Wang Honghai¹²^ORCID,Shang Liangliang³^ORCID

Affiliation:

1. College of Information Science and Engineering Northeastern University Shenyang China

2. The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

3. School of Electrical Engineering Nantong University Nantong China

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.24750

Reference37 articles.

1. Review on data-driven modeling and monitoring for plant-wide industrial processes

2. Data-driven Soft Sensors in the process industry

3. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

4. Improved canonical correlation analysis-based fault detection methods for industrial processes

5. Multimode non-Gaussian process monitoring based on local entropy independent component analysis

Cited by 2 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. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey;Processes;2024-01-24

2. GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method;The Canadian Journal of Chemical Engineering;2024-01-11

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