Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics-Reference-Cited by-同舟云学术

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Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics

Published:2018-03-20 Issue:14 Volume:57 Page:5045-5057
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhao Chunhui¹²^ORCID,Huang Biao²^ORCID

Affiliation:

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

2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G-2 V4, Canada

Funder

National Natural Science Foundation of China

Zhejiang Province

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.8b00233

Reference42 articles.

1. Online fault prognosis with relative deviation analysis and vector autoregressive modeling

2. Online average-based system modelling method for batch process

3. Constrained two dimensional recursive least squares model identification for batch processes

4. Multispace Total Projection to Latent Structures and its Application to Online Process Monitoring

5. Linearity Evaluation and Variable Subset Partition Based Hierarchical Process Modeling and Monitoring

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