中文摘要：

非平稳信号是由持续振荡成分和非持续振荡的瞬态成分混合而成的,因此很难利用线性方法对其进行有效分析。针对此问题,提出一种基于双重Q因子的非线性分析方法。这种方法将非平稳信号表示成高共振成分（高Q因子）和低共振成分（低Q因子）的叠加,进而用双重Q因子对信号进行稀疏分解,得到其高共振成分和低共振成分。将双重Q因子的分析方法用到轴承的早期故障诊断中,指出故障信号是由高共振成分和低共振成分故障冲击信号组成,从而用低Q因子提取出故障冲击成分,达到有效去除强噪声的目的,快速且准确地提取出轴承早期微弱故障的冲击特征。仿真信号和滚动轴承试验数据分析结果表明：该方法具有良好的降噪性能,能够有效地去除信号中的强噪声。

英文摘要：

Non-stationary signals are a mixture of sustained oscillations and non-oscillatory transients that are difficult to analyze by linear methods. Aiming at this problem, a nonlinear signal analysis method based on Q-factor is proposed, which expresses the non-stationary signal as the sum of a high-resonance （high Q-factor） and a low-resonance component （low Q-factor）. And then the dual Q-factor is used to make the signal be sparse-decomposed and the high-resonance component and low-resonance component of the signal are obtained. Applying this method to bearing early fault diagnosis, fault signals are made of high-resonance components and impulsion fault signals （low-resonance component）. And impulsion fault signals with strong background noise are extracted by low Q-factor and early impact characteristics of weak damage signals of the bearing are extracted successfully and quickly. The analysis results of simulating data and experimental data show that the proposed method has good denoising effect and it could remove the strong background noise effectively.