Probabilistic Process Monitoring

Abstract

To monitor industrial processes through a probabilistic manner, the probabilistic principal component analysis (PPCA) method has recently been introduced. However, PPCA has its inherent limitation that it cannot determine the effective dimensionality of latent variables. This chapter intends to introduce a Bayesian treatment upon the traditional principal component analysis method for process monitoring, which can automatically determine the effective number of retained principal components. Thus, a Bayesian principal component analysis-based monitoring approach can be developed. Besides, for those processes with multiple operation modes, the Bayesian regularization method is extended to its mixture form, and a mixture Bayesian regularization method of PPCA can be further developed for process monitoring. To combine the monitoring results in different operation modes, a probabilistic strategy is employed, based on which a mode localization approach is constructed, which can provide additional information and improve process comprehension for the operation engineer.