Robust monitoring and fault reconstruction based on variational inference component analysis

Probabilistic models such as probabilistic principal component analysis (PPCA) have recently caught much attention in the process monitoring area. An important issue of the PPCA method is how to determine the dimensionality of the latent variable space. In the present paper, one of the most popular Bayesian type chemometric methods, Bayesian PCA (BPCA) is introduced for process monitoring purpose, which is based on the recent developed variational inference algorithm. In this monitoring framework, the effectiveness of each extracted latent variable can be well reflected by a hyperparameter, upon which the dimensionality of the latent variable space can be automatically determined. Meanwhile, for practical consideration, the developed BPCA-based monitoring method is robust to missing data and can also give satisfactory performance under limited data samples. Another contribution of this paper is due to the proposal of a new fault reconstruction method under the BPCA model structure. Two case studies are provided to evaluate the performance of the proposed method.