【摘要】 为解决经验傅里叶分解方法(empirical Fourier decomposition, EFD)划分信号频谱边界过于密集且需要预设模态分量数目缺乏自适应性问题，提出基于能量谱线改进经验傅里叶分解(improved empirical Fourier decomposition method based on the energy spectral line, ESL-IEFD)的方法，并用于滚动轴承微弱故障诊断中。首先，计算轴承振动信号快速谱相关图的切片集成能量值，并用滤波平滑性能良好的S-G(Savitzky-Golay)算法进行处理，获得能量谱线；其次，以能量谱线局部最小值位置及频谱两端点作为分割边界，合理划分频谱进而自适应确定模态分量数目；然后，利用构建的零相位滤波器和逆傅里叶变换分别对各频段滤波和重构得到各分量；最后，进行包络谱分析，利用故障特征明显的分量诊断轴承故障。仿真和试验信号分析结果表明：相比EFD和优化EFD,ESL-IEFD方法在频谱频段划分上更合理不再过于密集；在故障诊断效果上，既能有效提取内、外圈单一微弱故障特征，又能对内、外圈复合故障特征进行分离与提取，能够准确诊断轴承故障类型。更多还原

【Abstract】 To solve the problems that the signal spectrum divided boundaries are overly dense and the number of modal components needs to be preseted together with lack of adaptability in the empirical Fourier decomposition(EFD) method, an improved empirical Fourier decomposition method based on the energy spectral line(ESL-IEFD) method was proposed and applied in the diagnosis of weak faults in rolling bearings. Firstly, the sliced integrated energy values of the fast spectral correlation diagram of the bearing vibration signal were calculated, and the S-G(Savitzky Golay) algorithm with good filtering and smoothing performance was used in processing to obtain the energy spectral lines. Secondly, using the local minimum position of the energy spectral line and the two endpoints of the spectrum as segmentation boundaries, the spectrum was reasonably divided and the number of modal components was adaptively determined. Then, the zero phase filter and inverse Fourier transform were applied to filter and reconstruct each component for each frequency band. Finally, the components with obvious fault characteristics in the envelope spectrum of each component were analysed to diagnose bearing faults. The simulated and experimental signal analysis results show that compared with the EFD and optimised EFD, the ESL-IEFD method is more reasonable and less dense in frequency band division, and can adaptively determine the number of modal components. In terms of fault diagnosis effect, it can effectively extract single weak fault features of inner and outer rings, as well as separate and extract composite fault features of inner and outer rings, and accurately diagnose bearing fault types.更多还原