Abnormal Condition Identification via OVR-IRBF-NN for the Process Industry with Imprecise Data and Semantic Information-Reference-Cited by-同舟云学术

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Abnormal Condition Identification via OVR-IRBF-NN for the Process Industry with Imprecise Data and Semantic Information

Published:2020-02-21 Issue:11 Volume:59 Page:5072-5086
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Yuan Jie¹^ORCID,Wang Shu¹²,Wang Fuli¹²,Zhang Shumei³

Affiliation:

1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

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

3. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Funder

National Natural Science Foundation of China

Key Laboratory of Process Automation in Mining and Metallurgy

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.9b06554

Reference40 articles.

1. Working condition recognition based on an improved NGLDM and interval data-based classifier for the antimony roughing process

2. A safe control scheme under the abnormity for the thickening process of gold hydrometallurgy based on Bayesian network

3. A review of the Expectation Maximization algorithm in data-driven process identification

4. Two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase batch process monitoring with uneven lengths

5. Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring

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1. Transferable intervalization and midpoint de-mixing interval independent component analysis for industrial fault detection under data uncertainty;Measurement;2024-08

2. Generalized Gaussian Distribution-Based Interval Principal Component Analysis (GGD-IPCA) for Anomaly Detection of Processes With Uncertainty;IEEE Transactions on Instrumentation and Measurement;2024

3. Vertices Packaging-Based Interval Independent Component Analysis (VP-I²CA) for Fault Detection With Process Uncertainty;IEEE Transactions on Industrial Informatics;2024-01

4. Application of local and global interval embedding algorithms in uncertain system processes;Measurement Science and Technology;2023-11-01

5. Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process;Processes;2023-08-12

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