计算机工程Issue(11)：139-142,148,5.DOI:10.3969/j.issn.1000-3428.2014.11.028
一种动态特征选取方法及其在故障诊断中的应用
A Dynamic Feature Selection Approach and Its Application in Fault Diagnosis
摘要
Abstract
According to the characteristic of fault data of high-speed train, a dynamic feature selecting algorithm is proposed to research the measured data of the running gear( referring mainly to bogie) of high-speed train. The approach combines the advantages of Fisher ratio and fuzzy entropy dynamically, which manages to evaluate features more accurately and removes the redundant features effectively to obtain superior feature subset by weighted average method. The new approach can improve classification accuracy. Experimental results for fault data of high-speed train show that the proposed approach not only improves the classification accuracies significantly,but also strengthens the stability in low speed. The overall-precise improvement is 5. 2% after extracting the optimal feature space in average compared with that of the original feature space.关键词
特征选取/模糊熵/Fisher比率/故障分类/相似性分类器/鲁棒性Key words
feature selection/fuzzy entropy/Fisher ratio/fault classification/similarity classifier/robustness分类
信息技术与安全科学引用本文复制引用
蔡斌斌,蒋鹏,金炜东,秦娜..一种动态特征选取方法及其在故障诊断中的应用[J].计算机工程,2014,(11):139-142,148,5.基金项目
国家自然科学基金资助重点项目(61134002)。 （61134002）
