摘要 : In advanced industrial fields such as chemical processes, faults must absolutely be detected; we cannot afford to operate with failing operative parts. It is therefore, necessary to take into consideration the uncertainties in ord... 展开 In advanced industrial fields such as chemical processes, faults must absolutely be detected; we cannot afford to operate with failing operative parts. It is therefore, necessary to take into consideration the uncertainties in order to establish a guaranteed failure detection. For this propose, several approaches have been introduced in the literature based on the Principal Components Analysis (PCA) approach for interval-valued data. However, these developed approaches treated only the linear process models. To overcome this drawback, recently, the researchers proposed to extend monitoring process based on Kernel Principal Components Analysis (KPCA) approach to the nonlinear uncertain case, in which two models are developed: model based on the lower bound (LB) and upper bound (UB) and model based on midpoints and radii. 收起