基于流形学习算法的齿轮变速箱故障特征提取  

Fault Feature Extraction of the Gearbox based on Manifold Learning Algorithm

作　　者：马维金[1] 张琳[1] 张纪平[1] 黄彬城[2] 罗朝阳[1] 

机构地区：[1]中北大学机械与动力工程学院,山西太原030051 [2]北京航空航天大学交通科学与工程学院,北京100191

出　　处：《机械传动》2015年第8期111-114,共4页Journal of Mechanical Transmission

基　　金：国家自然科学基金(51375327);山西省自然科学基金(2013011025-1);高等学校博士学科点专项科研基金(20131420120002)

摘　　要：流形学习算法是一种非线性的数据降维方法,可以获得数据的低维几何结构,能很好的体现系统的本质。为了提高齿轮变速箱振动故障信号的可分性,应用流形学习方法对齿轮变速箱振动信号进行故障特征提取。研究结果表明,流形学习方法可以有效地提取齿轮变速箱振动故障的特征信息,并能有效区分不同故障类型的特征信息。运用流形学习方法进行故障特征提取后的诊断结果与时域统计特征提取方法相比,提高了故障诊断的正确率。The manifold learning algorithm is a method of nonlinear data dimensionality reduction and can get available low dimensional geometry,it is also better reflects the nature of things. In order to improve the classification ability of gearbox vibration signals,a new feature extraction method from fault signals of gearbox vibration based on the manifold learning method（ MLM） is proposed. The results show that the fault feature information of gearbox vibration can be MLM effectively extracted and the different types of fault feature information can be separated. The diagnostic accuracy of features extracted by MLM is higher than that of the time domain statistical method.

关 键 词：特征提取 流形学习方法 局部线性嵌入法 故障诊断 

分 类 号：TH132.41[机械工程—机械制造及自动化]