噪声与振动控制2011，Vol.31Issue(2)：89-93,5.DOI:10.3969/j.issn.1006-1355-2011.02.022
基于EMD-SVD模型和SVM滚动轴承故障模式识别
Fault Pattern Recognition of Rolling Bearing Based on EMD-SVD Model and SVM
摘要
Abstract
According to the non-stationarity characteristics of the vibration signals from rolling bearing and the difficulty for obtaining enough fault samples, a comprehensive fault diagnosis method based on Empirical Mode Decomposition (EMD)，Singularity Value Decomposition (SVD), Renyi-entropy and Support Sector Machine (SVM) is proposed.Firstly, the denoised vibration signals are decomposed into a finite number of Intrinsic Mode Functions (IMF).Secondly, some IMF components are selected according to the criterion of mutual correlation coefficient between IMF components and denoised signal.Thirdly, the phase space of the selected IMF components is reconstructed so as to obtain the attractor orbit matrix.Fourthly, with the SVD method, singular value sequences are obtained, and then Renyi-entropies of these sequences are calculated as faulty eigenvector.Finally, the eigenvector serves as input of SVM classifier so that the faults of rolling bearing are recognized.Practical rolling bearing experiment data is used to verify this method, and the diagnosis results and comparative tests fully validate its effectiveness and generalization ability.关键词
故障诊断/滚动轴承/经验模式分解/奇异值分解/Renyi熵/支持向量机Key words
fault diagnosis / rolling bearing / empirical mode decomposition(EMD) / singularity value decomposition (SVD) / Renyi-entropy / support vector machine(SVM)分类
机械制造引用本文复制引用
吴虎胜,吕建新,来凌红,吴庐山,朱玉荣..基于EMD-SVD模型和SVM滚动轴承故障模式识别[J].噪声与振动控制,2011,31(2):89-93,5.基金项目
武警工程学院科研基金项目(WXK2009-17) （WXK2009-17）
