Feature selection for multivariate contribution analysis in fault detection and isolation

DOI10.1016/j.jfranklin.2020.03.005zbMath1441.93063OpenAlexW3012351477MaRDI QIDQ776095

F. A. Boldt, C. J. Munaro, Thomas Rauber

Publication date: 30 June 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.03.005

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Sensitivity (robustness) (93B35)

Related Items (4)

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes ⋮ Nonlinear process monitoring based on generic reconstruction-based auto-associative neural network ⋮ Multivariate statistical process monitoring based on principal discriminative component analysis ⋮ A novel feedback controller design with robust fault isolation ability

Cites Work

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• Solving fault diagnosis problems. Linear synthesis techniques
• Reconstruction-based contribution for process monitoring
• Probabilistic Principal Component Analysis
• 10.1162/153244303322753616
• Mathematics for Machine Learning
• Computational Methods of Feature Selection
• Fault detection and diagnosis in industrial systems

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