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• W2297211541 abstract "In this paper, a novel multimode batch process monitoring scheme called sparse representation residual space analysis (SR-RA) is proposed. The proposed SR-RA method first unfolds the three way batch data into a two way matrix, where each batch is represented as a vector. By utilizing robust sparse representation (SR), the proposed method decomposes the original high dimensional multimode data into three subspaces: the SR primal subspace which preserves all the mode relevant information, the Gaussian residual subspace which is single modal Gaussian distributed, and the outlier residual subspace which is sparsely distributed with only a few nonzero entries. As the fault information is all effectively captured in the two residual subspaces which are mode-irrelevant, fault detection is performed in the two residual subspaces. The proposed method can effectively deal with the multimode problem and it inherits the robustness of SR to large magnitude outliers. The superiority of the proposed SR-RA method is verified on a benchmark semiconductor etching problem." @default.
• W2297211541 created "2016-06-24" @default.
• W2297211541 creator A5076015499 @default.
• W2297211541 creator A5078201272 @default.
• W2297211541 date "2015-12-24" @default.
• W2297211541 modified "2023-09-26" @default.
• W2297211541 title "Sparse Representation Residual Space Analysis and Its Application to Multimode Batch Process Monitoring" @default.
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• W2297211541 doi "https://doi.org/10.1021/acs.iecr.5b02214" @default.
• W2297211541 hasPublicationYear "2015" @default.
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