Vibration signals of rolling element bearing’s early weak fault are often submerged by some interference components. To extract early weak fault features accurately, a weak fault feature enhancement method of rolling element bearing based on resonance sparse decompositionand multi-objective information frequency band selection is proposed. This method makes full use of resonance sparse decomposition in filtering the interferences and multi-objective information frequency band selection in enhancing impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. First, resonance sparse decomposition is used as the preprocessing program of multi-objective information frequency band selection to filter the interferences (such as rotating frequency with its harmonics) of rolling element bearing’s early weak fault characteristic components. Then, the filtered components containing main fault information are analyzed by MIFBS to establish the best band-pass filter. Finally, the envelope spectrum analysis is applied on the filtered vibration signal, and the fault characteristic frequency with its harmonics is extracted. To achieve the optimal output parameters of multi-objective information frequency band selection, fusion indexes based on time- and frequency-domain estimators are proposed and used to balance the enhancement of impulsive and cyclostationary characteristics of rolling element bearing’s early weak fault signals. Compared with most of the existing methods mainly based on single time-domain estimators or frequency-domain estimators to improve performance spectral kurtosis, the proposed fusion indexes overcome their defects and could enhance the impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. Effectiveness of the proposed method is verified through simulation and experiment. Besides, its advantage over the other related methods is also presented.

滚动轴承早期弱故障的振动信号经常被一些干扰因素淹没。为了准确地提取早期的弱故障特征，提出了一种基于共振稀疏分解和多目标信息频带选择的滚动轴承弱故障特征增强方法。该方法充分利用了共振稀疏分解来滤除干扰，利用多目标信息频带的选择来同时增强滚动轴承早期弱故障的脉冲和循环平稳特性。第一的，共振稀疏分解被用作多目标信息频带选择的预处理程序，以滤除滚动轴承早期弱故障特征分量的干扰（例如旋转频率及其谐波）。然后，通过MIFBS对包含主要故障信息的滤波后的分量进行分析，以建立最佳的带通滤波器。最后，对滤波后的振动信号进行包络谱分析，提取故障特征频率及其谐波。为了获得多目标信息频带选择的最佳输出参数，提出了基于时域和频域估计的融合指标，并用于平衡滚动轴承早期弱故障信号的脉冲和循环平稳特性的增强。与大多数现有的主要基于单个时域估计器或频域估计器以改善性能谱峰度的方法相比，所提出的融合指标克服了它们的缺陷，可以同时增强滚动轴承早期弱故障的脉冲和循环平稳特性。通过仿真和实验验证了该方法的有效性。此外，还介绍了其相对于其他相关方法的优势。与大多数现有的主要基于单个时域估计器或频域估计器以改善性能谱峰度的方法相比，所提出的融合指标克服了它们的缺陷，可以同时增强滚动轴承早期弱故障的脉冲和循环平稳特性。通过仿真和实验验证了该方法的有效性。此外，还介绍了其相对于其他相关方法的优势。与大多数现有的主要基于单个时域估计器或频域估计器以改善性能谱峰度的方法相比，所提出的融合指标克服了它们的缺陷，可以同时增强滚动轴承早期弱故障的脉冲和循环平稳特性。通过仿真和实验验证了该方法的有效性。此外，还介绍了其相对于其他相关方法的优势。