A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring

Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications. Variational Bayesian mixture of Canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises and model discrepancies. A sequential perturbation method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a Wastewater Treatment Plant (WWTP) simulated by Benchmark Simulation Model (BSM) with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy.