【摘要】 针对旋转设备工作环境复杂,难以提取轴承故障特征信息的问题,提出基于变分模态分解(VMD)和粒子滤波的故障诊断方法。首先,对原始振动信号进行VMD分解,得到有限个具有稀疏特性的固有模态函数(IMF);其次,利用基于峭度、相关系数、能量比的综合评价指标P,筛选最能反映原始信号故障特征的模态分量进行重构;最后,对重构故障特征分量进行粒子滤波,消除VMD残留的非线性、非高斯噪声后,利用包络谱分析故障类型。通过对实验轴承振动信号的分析,验证了该方法的准确性和有效性。更多还原

【Abstract】 Aiming at the problems that it is difficult to extract bearing fault characteristic information due to the complex working environment of rotating equipment, a fault diagnosis method based on Variational Mode Decomposition(VMD)and Particle Filter is proposed. First, the original vibration signal is decomposed using VMD to obtain a finite number of Intrinsic Mode Functions with sparse characteristics; Second, using the comprehensive evaluation index P based on kurtosis, correlation coefficient and energy ratio, the modal components that best reflect the fault characteristics of the original signal are filtered and reconstructed. Finally, particle filtering is performed on the selected fault feature components, and then analyze the type of fault using the envelope spectrum. Finally, particle filtering is performed on the reconstructed fault feature components to eliminate the non-linear and non-Gaussian noise of the VMD residuals, and the fault types are analyzed using the envelope spectrum. By analyzing the vibration signal of the experimental bearing, the accuracy and effectiveness of the method are verified.更多还原