A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring

Liu, Yiqi and Liu, Bin and Zhao, Xiujie and Xie, Min (2018) A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring. IEEE Transactions on Industrial Electronics, 65 (8). pp. 6478-6486. ISSN 0278-0046 (https://doi.org/10.1109/TIE.2017.2786253)

[thumbnail of Liu-etal-IEEE-TIE-2017-A-mixture-of-variational-canonical-correlation-analysis]
Preview
Text. Filename: Liu_etal_IEEE_TIE_2017_A_mixture_of_variational_canonical_correlation_analysis.pdf
Accepted Author Manuscript

Download (1MB)| Preview

Abstract

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 (SP) method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide a 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 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.