A Local Dynamic Broad Kernel Stationary Subspace Analysis for Monitoring Blast Furnace Ironmaking Process

Author:

Lou Siwei1ORCID,Yang Chunjie1ORCID,Wu Ping2ORCID

Affiliation:

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

2. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China

Funder

Key Programme of the National Natural Science Foundation of China

Social Development Project of Zhejiang Provincial Public Technology Research

Fundamental Research Funds of Zhejiang Sci-Tech University

Open Research Project of the State Key Laboratory of Industrial Control Technology

Zhejiang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Data-Driven Joint Fault Diagnosis Based on RMK-ASSA and DBSKNet for Blast Furnace Iron-Making Process;IEEE Transactions on Automation Science and Engineering;2024

2. One-Sided Relational Autoencoder With Seasonal-Trend Decomposition to Extract Process Correlations for Molten Iron Quality Prediction;IEEE Transactions on Instrumentation and Measurement;2024

3. Novel Data-Driven Deep Learning Assisted CVA for Ironmaking System Prediction and Control;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-12

4. Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes;The Canadian Journal of Chemical Engineering;2023-11-28

5. Quality-related Process Monitoring based on Analytic Stationary Subspace Analysis and Kernel Projection To Latent Structures;2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS);2023-09-22

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