基于k值优化VMD的滚动轴承故障诊断方法
Fault Diagnosis of Rolling Bearing Based on k-Optimized VMD

针对旋转机械中滚动轴承早期信噪比较低的故障特征提取困难问题，提出了一种基于能量的变分模式分解(variational mode decomposition, 简称VMD)模态数〖WTBX〗k〖WTBZ〗优化选取方法，用以提取滚动轴承早期故障特征，同时避免了信号分解过分或不足。首先，对振动信号进行VMD预分解，分别在不同〖WTBX〗k〖WTBZ〗值条件下计算分量信号能量与原始信号总能量；其次，根据基于能量的模态数〖WTBX〗k〖WTBZ〗选取准则，确定最佳模态数值对信号进行VMD分解；最后，通过峭度准则选择分量进行信号重构，对其进行包络分析，提取故障特征频率。将该方法运用到实际故障信号中，有效提取出滚动轴承内圈微弱故障特征，实现了早期故障特征判别，具有一定的应用价值和实际意义。
In the light of extracting fault features of a rolling bearing in early failure period, an incipient fault feature extraction method based on k-optimized variational mode decomposition (VMD) and kurtogram is proposed, where k value is selected based on energy criteria. First, the vibration signals are decomposed by VMD, the energy of intrinsic mode function components is summed under different k values. Based on the energy criterion, the best k value is determined, which is used as the parameter of VMD in decomposing the signals. The reconstructed signals are demodulated at last. The simulated and measured signals of fault bearing are analyzed by this method, which can effectively extract the weak inner ring fault feature. The result shows that the method can determine the incipient fault feature with the best k value, which is significantly valuable in applications.