Fault detection based on augmented kernel Mahalanobis distance for nonlinear dynamic processes

This paper presents a fault detection method based on augmented kernel Mahalanobis distance (AKMD) for monitoring nonlinear dynamic processes. In order to reflect the information of dynamic correlations, the measurements are stacked into augmented vectors at adjacent sampling instants. The augmented kernel Mahalanobis distance serves as the detection index, and its control limit is determined by the empirical method with assigning a significance level. Contrary to the mainstream of process monitoring methods based on principal component analysis (PCA), dimensionality reduction is not used here. The disadvantage of dimensionality reduction and space partition is discussed, and the improvement of fault detectability via data augmentation is analyzed. In addition, the computational complexity of the proposed method is acceptable. For training dataset containing m variables and n samples, if n ≫ m, the online computational burden of the proposed method is about O(n2). Simulations about a nonlinear dynamic process and the benchmark Tennessee Eastman process (TEP) both illustrate higher detection rates of the proposed method, compared with conventional multivariate statistical process monitoring (MSPM) methods such as PCA and its variants.