小波包样本熵灰色关联度轴承故障诊断∗

摘要

Extracting fault characteristics of rotating machinery from vibration signal has been a technical dif-ficulty. Aiming at nonlinearities and non-stationary of mechanical signals, a fault diagnosis method was pro-posed based on wavelet packet sample entropy and gray correlation degree. Firstly, the vibration signal was decomposed by wavelet packet, then the reconstructed signals in the first three sub larger sample entropy en-ergy generation are computed as the characteristic parameters. By calculating the gray correlation between i-dentified signal wavelet packet sample entropy and standard discriminant matrix to determine the working sta-tus and fault type of the bearing. multi-class sets of signals experiment results showing that: The status of bearing is different, the wavelet packet sample entropy is also different, and small impact of bearing load, can be used as an effective parameter for fault information,and verify the effectiveness of the proposed meth-od for fault identification.%从振动信号中提取故障特征一直是技术性难题，针对机械故障信号的非线性、非平稳性问题，提出一种小波包样本熵灰色关联度故障诊断方法。该方法首先对振动信号进行小波包分解，再计算重构信号中能量较大的前3个子代信号的非线性动力学参数样本熵作为特征参数。通过计算待识别信号小波包样本熵与标准故障特征向量判别矩阵中各元素之间的灰色关联度来判断轴承的工作状态和故障类型。对多组振动信号的实验结果表明：轴承不同状态的小波包样本熵不同，且受轴承负荷影响小，可作为表征故障的有效参数，并验证了所提方法用于故障识别的有效性。