Abstract
Fault detection can be taken as a behavior of detecting abnormal data in process data. Support vector data description (SVDD) has been successfully used for fault detection. Although density-induced support vector data description (D-SVDD) can give a better description of target data by introducing relative density degrees than SVDD, the problem of an additional parameter selection hinders the application of D-SVDD, which has a great influence on the performance of D-SVDD. This paper bounds this additional parameter for D-SVDD and applies D-SVDD to fault detection on TE process monitoring. Experiment shows D-SVDD is promising.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK20140008, and by the Soochow Scholar Project.
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Xue, Y., Zhang, L., Wang, B., Tang, B. (2018). Density-Induced Support Vector Data Description for Fault Detection on Tennessee Eastman Process. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_46
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DOI: https://doi.org/10.1007/978-3-030-04212-7_46
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