A KLMS Dual Control Chart Based on Dynamic Nearest Neighbor Kernel Space

Traditional projection dynamic monitoring methods focus on simultaneously decoupling the correlations among the process variables and the autocorrelations of the variables, which leads to a mixing problem of the two correlations. The mixing problem decreases the ability to model the dynamic correlations, thus decreasing the detection rates (DRs) of some faults. Considering that the projection matrix may cause the mixing of the two correlations, this article proposes a dynamic monitoring method based on directly monitoring original variables. This article first adopts the kernel least-mean-squares (KLMS) method to establish a univariate dynamic model, then adopts the univariate dynamic model and the dual control chart (DCC) to build the multivariate direct monitoring method, which is named the KL2C method (KL represents KLMS and 2 C represents DCC). Then, the dynamic nearest neighbor kernel space (DNNKS) is proposed to overcome the redundant dimensions problem of the KL2C method, which is named the DKL2C method (D represents DNNKS). Furthermore, based on the joint control chart, this article puts forward a dynamic monitoring method of two sequences, which both considers the advantages of the projection dynamic monitoring method and the direct dynamic monitoring method together. Finally, this article utilizes the Tennessee Eastman process and the continuously stirred tank reactor process to verify the effectiveness of the proposed methods.