Gaussian Mixture Model-Based Wasserstein Stationary Subspace Analysis for Process Monitoring-Reference-Cited by-同舟云学术

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Gaussian Mixture Model-Based Wasserstein Stationary Subspace Analysis for Process Monitoring

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Hanwen¹^ORCID,Liu Qingqing¹^ORCID,Jia Chao²^ORCID

Affiliation:

1. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

2. Software Engineering and Appraisal Center, China Electronics Standardization Institute, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10505814.pdf?arnumber=10505814

Reference32 articles.

1. Review of Recent Research on Data-Based Process Monitoring

2. Nonstationary Process Monitoring for Blast Furnaces Based on Consistent Trend Feature Analysis

3. Statistical process monitoring with independent component analysis

4. Non-Gaussian Industrial Process Monitoring With Probabilistic Independent Component Analysis

5. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis

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