Process monitoring and fault diagnosis is an important problem in chemical processes.Aiming at the real chemical process with its complicated mechanism

nonlinear characteristics and the numerous predictor variables within serious multicollinearity

a novel fault diagnosis model was constructed by combination of isometric feature mapping (ISOMAP) with linear discriminant analysis (LDA).The resulting discriminate model based on this data-driven approach ISOMAP-LDA was divided into two steps. In the first step that performed the ISOMAP manifold learning algorithm was in the original high space for nonlinear dimensionality reduction and feature extraction

and in the second step that built the LDA discriminate model was built in making use of the extracted feature variables.Finally

the proposed ISOMAP-LDA approach was applied to the multiple faults diagnosis of Tennessee Eastman chemical process.The results showed that this method had great power in nonlinear dimensionality reduction and strong generalization ability.At the same time

the ISOMAP-LDA fault diagnosis model was more concise and could be used to observe the structure of the set of samples in the embedding space.