Fault Detection and Diagnosis for Industry Process Based on Support Vector Data Description
- 10.2991/eame-18.2018.77How to use a DOI?
- fault detection; fault diagnosis; support vector data description; mutual information
A new approach for fault detection and diagnosis based on Support Vector Data Description (SVDD) has been proposed in this paper. Similar to the T2 and SPE statistic in principal components analysis (PCA), an appropriate nonlinear distance metric measured in feature space and threshold have been developed for fault detection. Once the fault is detected, fault diagnosis is then carried out using SVM based method. The fault diagnosis procedure is based on SVM and mutual information. The idea and effectiveness of the proposed algorithm are illustrated with respect to the simulation data collection from an illustrative example and the well-known Tennessee Eastman benchmark chemical process. Both the results show that the proposed approach works well to capture the underlying nonlinear process correlation thus providing a feasible and promising solution for nonlinear process monitoring.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Shuning Zhang AU - Hongyong Yang AU - Guanlong Deng PY - 2018/06 DA - 2018/06 TI - Fault Detection and Diagnosis for Industry Process Based on Support Vector Data Description BT - Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018) PB - Atlantis Press SP - 364 EP - 371 SN - 2352-5401 UR - https://doi.org/10.2991/eame-18.2018.77 DO - 10.2991/eame-18.2018.77 ID - Zhang2018/06 ER -