摘要
滚动轴承作为机械设备的重要部件,对机械设备的稳定运行起着重要的作用。滚动轴承的故障信号往往是多种信号的叠加,有必要对采集到的振动信号进行模式分解,进而基于模式识别方法实现对滚动轴承不同故障模式的分类识别。提出一种基于自适应局部迭代滤波(ALIF)和等距特征映射(ISOMAP)的机械设备故障分类识别方法。利用ALIF对滚动轴承的故障信号进行模式分解;对选定的模式分量提取多个统计学特征;最后利用ISOMAP对高维特征信号进行降维处理,实现对滚动轴承不同故障模式的分类识别。研究结果表明:所提方法在滚动轴承故障识别上具有良好的效果。
As an important part of mechanical equipment,rolling bearings play an important role in the stable operation of mechanical equipment.Commonly,the fault signal of rolling bearing is the superposition of multiple signals,so it is necessary to decompose the collected vibration signal,and then realize the identification of different fault mode of rolling bearing based on the pattern recognition method.A mechanical equipment fault classification and recognition method based on adaptive local iterative filtering(ALIF)and isometric feature mapping(ISOMAP)was proposed.ALIF was used to decompose the fault signals of rolling bearings;the multiple statistical features of selected mode components was extracted;finally,ISOMAP was used to perform dimensionality reduction processing on high-dimensional feature signals to realize the recognition of different failure modes of rolling bearings.The research results show that the proposed method has a good effect on the fault identification of rolling bearings.
作者
陈向俊
傅军平
于晓
陈栋栋
李黎苹
胡炳涛
冯毅雄
CHEN Xiangjun;FU Junping;YU Xiao;CHEN Dongdong;LI Liping;HU Bingtao;FENG Yixiong(Zhejiang Academy of Special Equipment Science,Hangzhou Zhejiang 310021,China;Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province,Hangzhou Zhejiang 310021,China;State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou Zhejiang 310027,China)
出处
《机床与液压》
北大核心
2023年第5期196-201,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金联合基金项目(U1709210)。
关键词
滚动轴承
自适应局部迭代滤波
等距特征映射
降维
故障识别
Rolling bearing
Adaptive local iterative filtering
Isometric feature mapping
Dimensionality reduction
Fault identification
