Structured Joint Sparse Orthogonal Nonnegative Matrix Factorization for Fault Detection
Author:
Affiliation:
1. School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, China
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
Funder
Natural Science Foundation of Beijing, China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10036023.pdf?arnumber=10036023
Reference51 articles.
1. Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection
2. Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
3. Fault Detection Using Structured Joint Sparse Nonnegative Matrix Factorization
4. Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis
5. Sparse canonical variate analysis approach for process monitoring
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1. The rise of nonnegative matrix factorization: Algorithms and applications;Information Systems;2024-07
2. A New End-to-End Monitoring Framework for Nonlinear Dynamic Processes With Unknown Noise Statistics;IEEE Transactions on Instrumentation and Measurement;2024
3. Non-Negative Matrix Underapproximation as Optimal Frequency Band Selector;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16
4. A Variable Projection-Based Algorithm for Fault Detection and Diagnosis;IEEE Transactions on Instrumentation and Measurement;2023
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