摘要
为更有效地利用齿轮振动信号进行故障诊断,提出基于改进局部均值分解(Local Mean Decomposition,LMD)和流形学习(ISOMAP)的齿轮故障特征提取方法。该方法将局部均值分解、模糊熵(Fuzzy Entropy,FE)和流形学习相结合。首先,利用LMD对原始振动信号进行多尺度分解,并在原LMD方法上添加自适应匹配波形以缓解端点效应对分解结果的影响;然后,对LMD分解后得到的乘积函数(Product Function,PF)进行模糊熵计算,获得原始信号不同尺度下的模糊熵数值,组成高维特征向量;最后,利用ISOMAP对高维特征向量进行二次特征提取,得到低维向量,进行故障识别。实际齿轮实验数据的处理结果表明该方法可以有效的诊断辨别齿轮故障,具有一定的优势。
In order to diagnosis gear fault efficiently by using vibration signal,a new method of gear fault based on local mean decomposition(LMD),fuzzy entropy and Isomap extraction is proposed,this method combines LMD,fuzzy entropy and Isomap. Firstly,by using the local mean decomposition(LMD) to decomposed the original vibration signal to obtain the components in different scales,and increases the adaptive matching waveform to alleviate the influence of end effects on decomposition results in the original LMD method. Then,considering fuzzy entropy can be use to distinguish the complexity of the signal effectively,so the fuzzy entropy of Product functions(PF) by LMD is calculated,a high-dimensional feature vector can be obtain with product functions. Finally,by using manifold learning(ISOMAP) on the high dimensional feature into low dimensional features which have better discrimination to describe different gear fault. It is applied to the gear experiment,the experimental results show that the method can effectively diagnose the gear faults and has certain superiority.
出处
《机械传动》
CSCD
北大核心
2018年第1期137-142,共6页
Journal of Mechanical Transmission
基金
国家自然科学基金(51605023)
长城学者培养计划(CIT&TCD20150312)
国家十三五重点研发计划城市轨道系统安全保障技术(2016YFB1200402)
城市轨道交通车辆服役性能保障北京市重点实验室引导性课题(06080915001)
北京建筑大学"城乡建设与管理"产学研联合研究生培养基地项目(cxy201728)
