Adaptive PCA based fault diagnosis scheme in imperial smelting process

In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio(GLR) test and Singular Value Decomposition(SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to eliminate false alarms and isolate faults efficiently.