Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

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 monit...

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Bibliographic Details
Published in:Journal of Central South University 2015-02, Vol.22 (2), p.601-609
Main Authors: Song, Yu, Jiang, Qing-chao, Yan, Xue-feng
Format: Article
Language:English
Subjects:
Engineering
Metallic Materials
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Summary: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.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-015-2561-3