Fault Diagnosis Method of Rolling Bearings Based on LMD and Singular Value Difference Spectrum

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.