Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference.

By considering autocorrelation among process data, canonical variate analysis (CVA) can noticeably enhance fault detection performance. To monitor nonlinear dynamic processes, a kernel CVA (KCVA) model was developed by performing CVA in the kernel space generated by kernel principal component analysis (KPCA). The Gaussian kernel is widely adopted in KPCA for nonlinear process monitoring. In Gaussian kernel-based process monitoring, a single learner is represented by a certain selected kernel bandwidth. However, the selection of kernel bandwidth plays a pivotal role in the performance of process monitoring. Usually, the kernel bandwidth is determined manually. In this paper, a novel ensemble kernel canonical variate analysis (EKCVA) method is developed by integrating ensemble learning and kernel canonical variate analysis. Compared to a single learner, the ensemble learning method usually achieves greatly superior generalization performance through the combination of multiple base learners. Inspired by the ensemble learning method, KCVA models are established by using different kernel bandwidths. Further, two widely used and monitoring statistics are constructed for each model. To improve process monitoring performance, these statistics are combined through Bayesian inference. A numerical example and two industrial benchmarks, the continuous stirred-tank reactor process and the Tennessee Eastman process, are carried out to demonstrate the superiority of the proposed method.