中文摘要：

滚动轴承常常在复杂工况下工作,当滚动轴承出现局部故障时,其振动信号中除了与故障信息相关的周期性瞬态冲击成分外,还包含轴转频等谐波成分和背景噪声。因此,在滚动轴承故障早期,对滚动轴承振动信号直接进行包络解调分析往往效果不佳。针对上述问题,提出了基于最优品质因子信号共振稀疏分解的滚动轴承故障诊断方法。该方法首先以信号共振稀疏分解低共振分量的峭度最大为目标,利用遗传算法对信号共振稀疏分解方法的品质因子进行优化,得到最优品质因子;然后利用最优品质因子对轴承振动信号进行信号共振稀疏分解,得到高共振分量和低共振分量;最后对低共振分量进行希尔伯特解调分析,提取轴承故障特征频率,进而诊断滚动轴承故障。仿真信号和试验信号的分析结果表明,该方法能有效提取轴承故障振动信号中的冲击成分,诊断轴承故障。

英文摘要：

When a localized defect is induced,the bearing vibration signal has the components of periodic impulses.However,the incipient periodic impulses are often submerged in the background noise and harmonic interferences,which undermine the effectiveness of envelope analysis method.To address the aforementioned issue,a novel method based on the resonance-based sparse signal decomposition with the optimal Q-factor is proposed in this paper.In this method,the optimal Q-factor is obtained firstly by the genetic algorithm,with the goal of maximizing the kurtosis of the low-resonance component of the resonance-based sparse signal decomposition.Then,the vibration signal of a rolling bearing is decomposed into the high-resonance component and the low-resonance component by the resonance-based sparse signal decomposition method with the optimal Qfactor.Finally,the low-resonance component is analyzed by the Hilbert envelope method,the cycle of the periodic impulse component can be acquired and the faults of the rolling bearing can be diagnosed.Simulation and application examples show that proposed method is effective in extracting the impulse signal from rolling bearings.