Joint Global and Local Discriminant Embedding for Multi-fault Process Monitoring and Fault Classification

This paper proposes a new manifold learning-based scheme for multi-fault detection and classification, which utilizes local and nonlocal embedding method to build a statistic index for fault detection and subsequently develops a joint global and local discriminant embedding (GLDE) approach to discover the discriminant features of multiple faults for fault classification. The proposed GLDE approach can capture the global and local/nonlocal structure information of complicated data and obtain the concise discriminant information for classification. Compared with the conventional Fisher discriminant analysis method, GLDE has a strong discriminant power and provides better monitoring results for complex multi-fault Tennessee Eastman process.