Subspace method aided data-driven fault detection based on principal component analysis

DOI10.1155/2017/1812989zbMath1400.93060OpenAlexW2769120669MaRDI QIDQ1794134

Xiangshun Li, Ling-ling Ma

Publication date: 15 October 2018

Published in: Journal of Control Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/1812989

zbMATH Keywords

principal component analysissubspace methoddata-driven fault detection

Mathematics Subject Classification ID

System identification (93B30) Eigenvalue problems (93B60) Observability (93B07) Realizations from input-output data (93B15)

Cites Work

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• Model-based fault detection, estimation, and prediction for a class of linear distributed parameter systems
• Subspace aided data-driven design of robust fault detection and isolation systems
• Model-based fault diagnosis techniques. Design schemes, algorithms and tools
• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• Subspace algorithms for the identification of multivariable dynamic errors-in-variables models
• Analytical redundancy and the design of robust failure detection systems
• Subspace model identification Part 1. The output-error state-space model identification class of algorithms

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