噪声与振动控制2017，Vol.37Issue(2)：143-147,5.DOI:10.3969/j.issn.1006-1355.2017.02.029
基于FIR-EMD和改进SVM的铁路轴承故障诊断
Fault Diagnosis of Railway Bearings Based on FIR-EMD and Improved SVM Algorithm
摘要
Abstract
It is difficult to effectively identify the faults in railway bearings. In this paper, a scheme for railway bearings fault diagnosis based on FIR-EMD and improved SVM algorithm is proposed. First of all, the signal is processed through the signal de-noising based on the FIR. Then, the de-noised vibration signals of the railway bearings are decomposed by EMD, and the IMF energy moments are calculated through the decomposed vibration signals. Finally, the IMF energy moments are used as feature vectors and input into the improved SVM to realize faults classification for the railway bearings. The results of an example show that the proposed diagnosis approach can effectively identify the fault patterns of railway bearings.关键词
振动与波/FIR/EMD/改进SVM/铁路轴承/故障诊断Key words
vibration and wave/FIR/EMD/improved SVM/railway bearings/fault diagnosis分类
机械制造引用本文复制引用
贺志晶,王兴,李凯,齐向东,徐殊宁,李冉..基于FIR-EMD和改进SVM的铁路轴承故障诊断[J].噪声与振动控制,2017,37(2):143-147,5.基金项目
国家国际科技合作专项资助项目(2014DFR70280) （2014DFR70280）
校青年科技研究基金资助项目(20133006) （20133006）
