Fault Diagnosis by Using Selective Ensemble Learning Based on Mutual Information

摘要：

Fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engines and the presence of multi-excite sources. There have been previous attempts to solve this problem by using artificial neural networks and others methods. In this paper, a novel algorithm named MISEN (Mutual Information based Selective Ensemble) is proposed to improve diagnosis accuracy and efficiency. MISEN is compared with the general case of bagging and GASEN, a baseline method, namely Genetic Algorithm Based Selective ENsemble, on UCI data sets. Then,MISEN is used to diagnose the diesel engine. Computational results show that MISEN obtains higher accuracy than other several methods like bagging of neural networks and GASEN.

关键词： Fault diagnosis Mutual information Selective ensemble learning Bagging Support vector machines

作者: Tian-Yu Liu Guo-Zheng Li

作者单位: School of Electric, Shanghai Dianji University, Shanghai 200040, China Department of Control Science and Engineering, Tongji University, Shanghai, 201804 China

会议类型: 国际会议

会议名称: The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)

会议地点: 云南丽江

会议语种:英文

页码: 191-197

在线出版日期: 2008-10-31（万方平台首次上网日期，不代表论文的发表时间）