Probabilistic process monitoring with Bayesian regularization

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 paper 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 is developed. A case study of the Tennessee Eastman (TE) benchmark process shows the feasibility and efficiency of the proposed method.