Variational Bayesian Student’s-t Mixture Model With Closed-Form Missing Value Imputation for Robust Process Monitoring of Low-Quality Data-Reference-Cited by-同舟云学术

Variational Bayesian Student’s-t Mixture Model With Closed-Form Missing Value Imputation for Robust Process Monitoring of Low-Quality Data

Published:2024-01 Issue:1 Volume:54 Page:373-386
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Dai Qingyang¹^ORCID,Zhao Chunhui¹^ORCID,Zhao Shunyi²^ORCID

Affiliation:

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

2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10367425/10004695.pdf?arnumber=10004695

Reference42 articles.

1. Learning Latent Representation for IoT Anomaly Detection

2. Whole Process Monitoring Based on Unstable Neuron Output Information in Hidden Layers of Deep Belief Network

3. Multisource-Refined Transfer Network for Industrial Fault Diagnosis Under Domain and Category Inconsistencies

4. An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring

5. Recursive Exponential Slow Feature Analysis for Fine-Scale Adaptive Processes Monitoring With Comprehensive Operation Status Identification

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