关键词: 滚动轴承, 自相关分析, 局域均值分解(LMD), 故障诊断

Abstract:

Rolling bearing fault vibration signals were nonstationary and multi-component modulated signals, especially in the early stages of the fault, fault features were difficult to identify because early fault signals were weak due to the weak modulation source and contained strong noise.The autocorrelation analysis and LMD method was proposed herein, it can extract fault features effectively. First, the noises were eliminated by using autocorrelation analysis, the periodic components in the signals were extracted. Second, multi-component modulation signals were decomposed into a number of product functions(PFs). Last, applying demodulated resonance technique to each  PF component, the fault feature frequency was extracted through envelopment analysis. The experimental results demonstrate the effectiveness of the method.

Key words: rolling bearing, autocorrelation analysis, local mean decomposition(LMD), fault diagnosis