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Development of a new soft sensor method using independent component analysis and partial least squares
Author(s) -
Kaneko Hiromasa,
Arakawa Masamoto,
Funatsu Kimito
Publication year - 2009
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11648
Subject(s) - soft sensor , independent component analysis , partial least squares regression , fault detection and isolation , component (thermodynamics) , variable (mathematics) , measure (data warehouse) , process (computing) , computer science , data mining , sample (material) , principal component analysis , sensitivity (control systems) , pattern recognition (psychology) , artificial intelligence , fault (geology) , regression analysis , machine learning , engineering , mathematics , electronic engineering , mathematical analysis , chemistry , physics , chromatography , actuator , thermodynamics , operating system , seismology , geology
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