故障模式识别技术作为故障诊断过程的重要方法,在滚动轴承故障诊断领域得到了越来越多的重视和应用。经验模式分解(Empirical Mode Decomposition,EMD)非常适合处理非线性、非平稳过程,它是Hilbert-Huang变换理论的一部分。对EMD分解得到的各个内禀模态函数(Intrinsic Mode Function,IMF)建立AR模型,可以实现非平稳、非线性过程的模型参数估计,能够有效的对滚动轴承故障进行诊断。 As an important method of fault diagnosis, fault pattern recognition technology has received more and more attention and application in the field of fault diagnosis of rolling bearings. Empirical Mode Decomposition (EMD) is well suited for nonlinear and non-stationary processes and is part of the Hilbert-Huang transform theory. The establishment of AR model for each intrinsic mode function (IMF) obtained by EMD decomposition can estimate the model parameters of non-stationary and nonlinear processes, and can effectively diagnose the fault of rolling bearing.