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- W2087285421 abstract "Abstract Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA‐based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model. © 2008 American Institute of Chemical Engineers AIChE J, 2009" @default.
- W2087285421 created "2016-06-24" @default.
- W2087285421 creator A5013821405 @default.
- W2087285421 creator A5082283686 @default.
- W2087285421 creator A5087461964 @default.
- W2087285421 date "2008-10-29" @default.
- W2087285421 modified "2023-10-12" @default.
- W2087285421 title "Development of a new soft sensor method using independent component analysis and partial least squares" @default.
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- W2087285421 doi "https://doi.org/10.1002/aic.11648" @default.
- W2087285421 hasPublicationYear "2008" @default.
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