中文摘要：

针对滚动轴承故障振动信号的非线性非平稳特性及强噪声特性，提出了一种基于局部均值分解（local mean decomposition，LMD）和奇异值差分谱的滚动轴承故障诊断方法．首先对原始信号进行LMD分解，得到若干乘积函数（productfunction，PF）分量，然后对故障特征明显的分量构建Hankel矩阵并进行奇异值分解，求出奇异值差分谱曲线，找到奇异值差分谱最大突变点来确定奇异值重构分量的个数，进而对包含故障特征频段的分量进行消噪和重构，再对重构信号进行Hilbert包络谱分析，提取故障特征．实验结果和工程应用表明：LMD和奇异值差分谱结合的信号特征提取方法，能准确、有效地提取滚动轴承的故障特征频率，对故障类型作出准确判断．

英文摘要：

Aiming at the nonlinear and non-stationary vibration signal of the rolling bearing, a method based on local mean decomposition （LMD） and singular value difference spectrum is proposed. First, the original vibration signal was decomposed into several product functions （PFs） by LMD, Hankel matrix is constructed by the product function that contains the fault information, and the singular value difference spectrum can be obtained after singular value decomposition. Then, the maximum catastrophe point is used to identify the number of singular value reconstruction components; therefore, the original component is reconstructed and the noise is restrained. Finally, the reconstructed signal is demodulated by Hilbert transformation to extract the fault feature. Results of experiment and engineering signals analysis show that the method combined LMD and singular value difference spectrum can accurately extract the fault feature of rolling bearing for diagnosis.