Multiple Condition Indicator Decoupling and Hierarchical Monitoring for Large-Scale Nonstationary Processes-Reference-Cited by-同舟云学术

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Multiple Condition Indicator Decoupling and Hierarchical Monitoring for Large-Scale Nonstationary Processes

Published:2022-03-29 Issue:14 Volume:61 Page:4896-4909
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Chen Xu¹,Hu Yunyun¹,Zhao Chunhui¹²^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

Funder

National Natural Science Foundation of China

State Key Laboratory of Industrial Control Technology

National Science Fund for Distinguished Young Scholars

State Key Laboratory of Synthetical Automation for Process Industries

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c00013

Reference44 articles.

1. Ward, J. S.; Barker, A. Undefined by data: a survey of big data definitions. arXiv e-prints, 2013-09-20, https://arxiv.org/abs/1309.5821v1.

2. Layer-by-Layer Enhancement Strategy of Favorable Features of the Deep Belief Network for Industrial Process Monitoring

3. Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis

4. Clustered Manifold Approximation and Projection for Semisupervised Fault Diagnosis and Process Monitoring

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3. Sparse Full Space Industrial Process Monitoring Method Based on Deep Autoencoder Neural Network;2023 China Automation Congress (CAC);2023-11-17

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