A novel dynamic Baysian canonical correlation analysis method for fault detection

In the field of Multivariate Statistical Process Monitoring (MSPM), process dynamics has always been the focus. Besides, considering the uncertainty in chemical processes, latent variable models are extended to the probabilistic framework, in which maximum likelihood estimation with expectation maximization (EM) algorithm is adopted for parameter learning. However, the modelling performance is restricted owing to the reason that these models either neglect the static characteristics reflecting process structure or suffer from over fitting and local optimum. To tackle these issues, a dynamic Baysian canonical correlation analysis (DBCCA) model is developed through combining the consideration of process dynamics with the variational CCA and utilized for fault detection. More specifically, both static structural characteristics and process dynamics can be simultaneously captured in DBCCA model. In essence, the variational Bayesian approach renders effects of regularization, alleviating the dilemma in traditional maximum likelihood estimation methods by nature. The effectiveness of proposed method is testified on the well-known Tennessee Eastman (TE) benchmark, where improvements are attained.