Abstract
Principal component analysis (PCA) has been widely utilized for process monitoring owing to its simplicity, easy understanding and high efficiency in dealing with large numbers of process variables. Aimed at its disadvantages including relatively low detectability, this paper mainly presents a modified PCA (MPCA) approach based on the combination of the two test statistics for process monitoring. Thus, the monitoring chart will be reduced to one and the fault detectability will be improved. Then, several other PCA-based approaches are described briefly. Besides, theoretical discussions are made among these schemes to demonstrate the virtues of MPCA. The results of theoretical analysis indicate that MPCA-based approach has lower computational complexity and higher fault detection rate. An industrial benchmark of Tennessee Eastman (TE) process is employed to demonstrate the effectiveness of MPCA-based approach according to the comparison of simulation consequences.
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