Abstract
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.
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The work is supported by Research Fund of Nantong (MS12016036) and University Research Fund of Jiangsu Province (17KJB530008).
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Lu, C., Wang, J. Joint Global and Local Discriminant Embedding for Multi-fault Process Monitoring and Fault Classification. Arab J Sci Eng 43, 5859–5869 (2018). https://doi.org/10.1007/s13369-018-3072-y
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DOI: https://doi.org/10.1007/s13369-018-3072-y