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- W4293518918 abstract "Standard nonnegative matrix factorization (NMF) is a powerful algorithm for data analysis and processing. In this paper, we improve the standard NMF and propose a supervised NMF algorithm that uses labeled samples for model training. The new NMF can identify the label of samples and achieve clustering. Using the clustering property of the supervised NMF algorithm, fault detection and isolation (FDI) is developed when both fault-free samples and all types of faulty samples are available for model training. Finally, the effectiveness of the proposed FDI approach is verified by an experiment on FDI of the penicillin fermentation process (PFP)." @default.
- W4293518918 created "2022-08-30" @default.
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- W4293518918 date "2022-08-03" @default.
- W4293518918 modified "2023-10-18" @default.
- W4293518918 title "A Fault Detection and Isolation Method Based on Supervised Nonnegative Matrix Factorizations" @default.
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- W4293518918 doi "https://doi.org/10.1109/ddcls55054.2022.9858573" @default.
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