Fault Detection Based on Kernel Global Local Preserving Projection

In this paper, a fault detection method based on kernel global local preserving projection is presented to address the nonlinear characteristics of industrial systems. First, data are projected into a high-dimensional feature space through nonlinear mapping, enabling linear separability in this feature space. Subsequently, data features are extracted using the global local preserving projection method in the high-dimensional feature space. Finally, a monitoring model is established based on these features. Experiments on the Tennessee Eastman process and industrial boilers demonstrate that the proposed method balances global and local data structures, reduces nonlinear influences, and improves the fault detection rate.