Bearing fault diagnosis based on EMD and PSD

This paper presents a new method which combines empirical mode decomposition (EMD) and power spectral density (PSD) together for bearing fault diagnosis in low speed-high load rotary machine. EMD is a novel self-adaptive method which is based on partial characters of the signal. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Then calculate the PSD of each IMF. The results of application in simulation signal and practical bearing fault signal both show its efficiency.

[1]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[2]  Robert X. Gao,et al.  Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition , 2008 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  N. Huang,et al.  System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes , 2003 .

[5]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[6]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[7]  Yonghong Peng,et al.  Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage , 2006 .

[8]  Henk Toersen,et al.  Application of an envelope technique in the detection of ball bearing defects in a laboratory experiment , 1998 .

[9]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[11]  Robert X. Gao,et al.  Vibration Analysis of a Sensor-Integrated Ball Bearing , 2000 .