Multiple Sensor Fault Diagnosis Based on Multiway Principal Component Analysis

Monitoring batch processes to ensure their safe operation and to produce consistently high-quality products is needed. Multiway principal component analysis(MPCA) is a nonlinear modeling methodology for batch process. Previous publications have focused upon the application of statistical analysis for sensor fault identification through data reconstruction. These reconstruction based methods do not address the problem of fault propagation to other sensor measurements and as a consequence misleading fault identification can result. Based on MPCA, this paper use a multiple sensor faults diagnosis method. By using the T~2-statistic in conjunction with the associated contribution plot, multiple sensor faults can be identified in a sequential manner. Simulations verify the effectiveness of the method.