Detection of process model changes in PCA based performance monitoring

The detection of process changes through a principal component analysis based monitoring scheme can be achieved through the interrogation of two metrics, Hotelling´s T² and the Q-statistic. The Q-statistic has been shown to be insensitive to small changes in the process model parameters. In this paper, a modified statistic based on the local approach is proposed to detect changes in model parameters in a principal component analysis monitoring scheme. The performance of the more traditional Q-statistic is compared with the modified statistic through their application to fault detection in a continuous stiffed tank reactor.