A novel data-driven process monitoring method named Fisher discriminant global-local preserving projection (FDGLPP) is proposed and applied to diagnosis fault in industrial process. Integrating Fisher discriminant analysis with global-local preserving projection to solve the optimal projection direction, then transforming original data set with the projection matrix can not only preserve global manifold structure and local neighborhood structure of the data set but also promise the discrimination of the projection subspace, which can afford better fault identification performance. On the other hand, Kernel density estimation is introduced to calculate a more accurate control limit of monitoring indexes, which improves process monitoring performance with less false alarm and detection delay. A numerical simulation is introduced to validate the better dimension reduction performance of FDGLPP algorithm. And a case study on Tennessee Eastman process demonstrates the advantages of proposed method in fault detection and identification contrast with GLPP, LPP and PCA method. Then, t-SNE is applied to visualization the discrimination of FDGLPP. (C) 2019 Elsevier Ltd. All rights reserved.