Joint Global and Local Discriminant Embedding for Multi-fault Process Monitoring and Fault Classification
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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.
KeywordsFault detection and classification Manifold learning Global and local discriminant embedding Multi-fault process Tennessee Eastman process
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The authors declare that there is no conflict of interests regarding the publication of this paper.
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