A Virtual Sample Generation Approach for Blast Furnace Fault Diagnosis

Abstract:

The blast furnace faults will cause significant economic and human losses. Therefore, the study of blast furnace intelligent fault diagnosis technology is necessary and important. But some real fault data is difficult to obtain .The training set can not be provided enough samples.So a new virtual fault samples generation method is designed to get enough tainning samples. The designed method uses group discovery technique and the Box-Muller method to generate the candidate virtual fault sample set. And then with the help of manifold contraction and the semi-supervised learning algorithm to select the virtual samples with better performance. By adding the selected virtual samples to the original training set,a new svm fault diagnosis classifier is obtained. The results show that this method can simulate the fault samples effectively,and the new classifier’s accuracy is much more acceptable.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1379-1384
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1379
• Online since: February 2013
• Authors: Mo Sha, An Na Wang, Mao Xiang Chu, Limei Liu
• Keywords: Fault Diagnosis, Manifold Contraction, Virtual Sample Generation

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