Enhanced Fault Detection Based on Ensemble Global–Local Preserving Projections with Quantitative Global–Local Structure Analysis

A novel data-driven fault detection strategy that fully considers the global–local structure is proposed. Inevitably, traditional methods concerning the global–local structure are unilateral and the global–local information is extracted partially. To extract the information fully, an available idea is to combine these methods. Considering the feasibility, the same type of model is applied in this paper. The global–local preserving projections (GLPP) model, which is suitable for extracting the global–local structure, is used as the base model of the proposed model. The purpose of this paper is to study the combination of the GLPP models by the use of ensemble learning strategy. This idea requires dealing with two main problems: how to choose the GLPP models diversely and how to combine them. To solve these two issues, first, a global index Gper that could analyze the global and local structure quantitatively is proposed and then the diverse GLPP models can be selected according to Gper. The kernel density ...