摘要
结合模糊粗糙集(FRS)理论和支持向量机(SVM)分类机理,提出了一种新的液压系统故障诊断方法。应用FRS理论处理不确定、不完备信息的属性约简能力,剔除冗余信息,获得具有代表性的特征信息,再利用SVM的推广能力,对小样本数据进行故障诊断。通过此方法对采煤机牵引部液压系统的故障诊断仿真实验,结果证明大大提高了诊断精度和效率。
The dissertation brings forward a new method in hydraulic system fault diagnosis based on Fuzzy-rough set （FRS） theory and support vector machine （SVM）. FRS has strong attribute reduction ability in handling uncertain and incomplete information by removing the redundant parts and getting the representative features. SVM is very efficient in classification and regression. Can carry out fault diagnosis in small sample of data using FRS theory combined with SVM. It is proved that higher diagnostic precision and higher efficiency can achieved by applying the new method in the hydraulic system fault diagnosis simulation of the shearer traction division.
出处
《煤矿机械》
北大核心
2010年第2期201-203,共3页
Coal Mine Machinery
关键词
液压系统
故障诊断
模糊粗糙集
支持向量机
hydraulic system
fault diagnosis
fuzzy-rough set（FRS）
support vector machine（SVM）
