噪声与振动控制2017，Vol.37Issue(4)：171-174,4.DOI:10.3969/j.issn.1006-1355.2017.04.033
基于MDS和神经网络的滚动轴承故障诊断方法
Rolling Bearing Fault Diagnosis Method Based on MDS and Neural Network
摘要
Abstract
A new fault diagnosis method for rolling bearings based on Multidimensional Scaling(MDS)and neural network is put forward. First of all, several time-domain statistics indexes of rolling bearings are extracted from original signals. Then, the indexes containing fault information are processed by MDS to reduce the data dimension. Finally, the low dimensional characteristic indexes are served as input parameters of neural network to identify fault patterns of the rolling bearings. The analysis results from rolling bearing signals with rolling element, inner-race and out-race faults show that the approach of neural network diagnosis based on MDS is superior to that without MDS and can identify roller's fault patterns effectively.关键词
振动与波/滚动轴承/多维尺度分析/神经网络/故障诊断Key words
vibration and wave/rolling bearing/multidimensional scaling/neural network/fault diagnosis分类
机械制造引用本文复制引用
马朝永,黄攀,胥永刚,付胜..基于MDS和神经网络的滚动轴承故障诊断方法[J].噪声与振动控制,2017,37(4):171-174,4.基金项目
国家自然科学基金资助项目(51375020) （51375020）
北京市优秀人才培养资助项目(2011D005015000006) （2011D005015000006）
