基于加权联合提升包络谱的轴箱轴承故障诊断

Since the weak fault feature of train axle-box bearings is difficult to be extracted in a wide frequency band, this paper proposes a weighted combined improved envelope spectrum (WCIES) for fault diagnosis based on the second-order cyclostationary of bearing fault signals. First, the fine demodulation of the vibration signal in the full frequency band is achieved by decomposing the vibration signal into the dual-frequency domain composed of spectral and cyclic frequencies through the spectral coherence algorithm. The candidate fault frequency of the bearing is identified based on the local feature of spectral coherence. Then, the 1/3-binary tree filter is applied to divide the spectral frequency into a series of narrowbands with different center frequencies and bandwidths, and the mode of spectral coherence is integrated along the spectral frequency in the narrow band to obtain the narrowband IES. Then, the CIES of each decomposition layer is constructed by taking the ratio of the energy of the candidate fault frequency in the narrowband IES as the diagnostic index. Finally, the weighted average of the WCIES of different decomposition layers is performed, and the WCIES of the bearing vibration signal is obtained. The research results show that the advantage of the proposed method is that it can fully integrate the bearing fault information distributed in different narrowbands and does not depend on the nominal fault period information. Compared with the existing methods, it can more effectively reveal the characteristic frequency and harmonic characteristics of bearing faults and has advantages in extracting and identifying weak faults of axle-box bearings.

为解决列车轴箱轴承微弱故障特征在宽频带上难以提取的问题，基于轴承故障信号的二阶循环平稳特 性，提出了一种利用加权联合提升包络谱进行故障诊断的方法. 首先，利用谱相干算法将振动信号分解到由频谱 频率和循环频率构成的双频域，实现振动信号在全频带内的精细化解调，并基于谱相干的局部特征识别轴承候选 故障频率；接着，利用 1/3 二叉树滤波器将频谱频率分割为不同中心频率和带宽的窄带，在窄带内沿着频谱频率 对谱相干的模进行积分，得到窄带提升包络谱；然后，以候选故障频率在窄带提升包络谱中的能量占比为诊断性 指标，在每一分解层上构造联合提升包络谱；最后，对不同分解层的联合提升包络谱进行加权平均，得到轴承振动 信号的加权联合提升包络谱. 轨道车辆轴箱轴承台架试验信号的研究结果表明：所提方法的优势在于能充分整合 分布于不同窄带内的轴承故障信息，且不依赖于名义故障周期信息；和现有方法相比，能更有效地揭示轴承故障 特征频率及其谐波特征，在提取和识别轴箱轴承微弱故障方面具有一定优势.