Local class-specific discriminant analysis with variable weighting and its application in fault diagnosis

Abstract In this paper, local class-specific discriminant analysis with variable weighting (VW-LCSDA) model was developed for fault classification in industrial processes. Although FDA has been extensively studied in the field of fault diagnosis as a classic method of dimension reduction and classifying, the effectiveness of the FDA is degraded in many cases due to data distribution and number of classes. The proposed method weights the variable vectors by evaluating the impact of the fault on each feature. Besides, the in-class and out-of-class scatter matrixes of class-specific discriminant analysis (CSDA) are used for dimension reduction and classification. In addition, the local structure of the sample data is preserved with reference to the LPP(Locality preserving projection) method, which is used to optimize the scatter matrix. The proposed fault classification method is validated by the Tennessee-Eastman process. The simulation results show that the VW-LCSDA method is a fault diagnosis method superior to FDA, CSDA and variable-weighted CSDA (VW-CSDA).

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