Abstract
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern (SP) framework integrated with a self-organizing map (SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman (TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes. Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
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Foundation item: Project(2013CB733605) supported by the National Basic Research Program of China; Project(21176073) supported by the National Natural Science Foundation of China; Project supported by the Fundamental Research Funds for the Central Universities, China
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Song, Y., Jiang, Qc. & Yan, Xf. Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map. J. Cent. South Univ. 22, 601–609 (2015). https://doi.org/10.1007/s11771-015-2561-3
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DOI: https://doi.org/10.1007/s11771-015-2561-3