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- W2356157032 abstract "Principal component analysis(PCA) is a classical modeling method and has been successfully applied in the process monitoring system for power plant boilers.However,the problem how to select sample data in PCA model building has not been solved.The relationship between fault detectability of PCA model and standard deviation of sample data was derived.The formula indicates that PCA model has better fault detectability using the sample data with bigger standard deviation.Two PCA models were built with on-site data from a 600 MW power plant boiler to validate the relationship.The results show that stronger fluctuant sample data should be selected to build better PCA model." @default.
- W2356157032 created "2016-06-24" @default.
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- W2356157032 date "2009-01-01" @default.
- W2356157032 modified "2023-09-25" @default.
- W2356157032 title "Data Selection in Principal Component Analysis for Power Plant Boiler Modeling" @default.
- W2356157032 hasPublicationYear "2009" @default.
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