滚动轴承故障诊断的品质因子可调小波重构方法

针对轴承早期故障诊断困难的问题,提出了基于信号共振稀疏分解与品质因子可调小波重构的滚动轴承故障诊断方法.该诊断方法首先对轴承故障信号进行共振稀疏分解获得高共振分量和低共振分量;然后对低共振分量进行品质因子可调小波重构,并结合峭度分析,筛选出最佳分析信号;最后对最佳分析信号进行希尔伯特解调分析,从而提取滚动轴承故障特征信息.通过对仿真信号和实际故障信号进行分析,该方法能有效提取轴承故障信号中的冲击成分,凸显故障特征.

Fault diagnosis of rolling bearing based on tunable-Q wavelet reconstruction

To overcome the difficulty of early fault diagnosis for the rolling bearing, a method for the fault diagnosis of rolling bearings based on the resonance-based sparse signal decomposition and the tunable-Q wavelet reconstruction was proposed. In this method, the vibration signal of a rolling bearing was decomposed into the high-resonance component and the low-resonance component by the resonance-based sparse signal decomposition. Then, the low-resonance component was further decomposed into a set of sub-signals by the tunable-Q wavelet method and the proper signal was reconstructed from some selected sub-signals combined with kurtosis analysis. Finally, the proper signal was analyzed by the Hilbert demodulation method and the fault characteristics of the rolling bearing could be extracted. Simulation and application examples show that the proposed method is effective in extracting impulse signal from vibration signal of rolling bearing and making the fault characteristics more prominent.