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• W2890863938 abstract "In order to improve the performance of fault isolation and diagnosis of principal component analysis (PCA) based methods, this article proposes a novel fault detection and isolation approach using the structured joint sparse PCA (SJSPCA). The objective function involves two regularization terms: the <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$l_{2,1}$</tex-math></inline-formula> norm and the graph Laplacian. By imposing the <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$l_{2,1}$</tex-math></inline-formula> norm, SJSPCA is able to achieve row-wise sparsity, and introducing the graph Laplacian term can incorporate structured variable correlation information. The row-sparsity property of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$l_{2,1}$</tex-math></inline-formula> norm ensures that the score indices associated with normal variables approaching zero and the graph Laplacian constraint helps the isolation of correlated faulty variables. Once a fault is detected, a two-stage fault-isolation strategy is considered and a score index is calculated for each variable. It is proved that the proposed two-stage strategy is capable of isolating faulty variables. The improved fault-isolation performance of SJSPCA is illustrated by a simulation example and a gas flow fault observed in an industrial blast furnace iron-making process." @default.
• W2890863938 created "2018-09-27" @default.
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• W2890863938 date "2019-05-01" @default.
• W2890863938 modified "2023-10-18" @default.
• W2890863938 title "Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation" @default.
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• W2890863938 doi "https://doi.org/10.1109/tii.2018.2868364" @default.
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