Data-driven models for fault detection using kernel PCA: A water distribution system case study

DOI10.2478/v10006-012-0070-1zbMath1283.93330OpenAlexW2002646710MaRDI QIDQ5403406

Michał Grochowski, Kazimierz Duzinkiewicz, Adam Nowicki

Publication date: 26 March 2014

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2478/v10006-012-0070-1

zbMATH Keywords

fault detectionmonitoringmachine learningkernel PCAwater leakage detection

Mathematics Subject Classification ID

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Cites Work

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• Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds
• Process fault detection based on modeling and estimation methods - a survey
• Kernel PCA for novelty detection
• KPCA for semantic object extraction in images
• Nonlinear model predictive control of a boiler unit: A fault tolerant control study
• A complete gradient clustering algorithm formed with kernel estimators

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