滑动轴承的运行模态参数是其状态监测和早期故障诊断的重要指标,利用随机子空间法识别滑动轴承运行模态参数时,环境噪声和阶数过估计引起的虚假模态会影响真实模态参数的识别.为减少虚假模态的干扰,首先对振动信号利用互补集合经验模态分解和小波变换相结合的方法进行降噪处理,然后将预处理后的信号分段并分别进行模态参数识别,通过对比同阶极点获得更清晰的稳定图,最后采用谱聚类算法实现模态参数的自动选择.通过数值仿真和相关试验验证该方法的有效性.
Operational modal parameters are the important indicators for condition monitoring and early fault diagnosis of journal bearings.In operational modal parameters identification for journal bearings using stochastic subspace identification,spurious modes due to background noise and order overestimation can seriously affect the identification of real modes.In order to reduce the interference of spurious modes,denoising of vibration signals is carried out using complementary ensemble empirical mode decomposition and wavelet transform.And signals are then segmented separately for modal parameters identification.A clear stabilization diagram is obtained by comparing the same order poles.Finally,hierarchical clustering analysis is conducted to extract modal parameters automatically.The effectiveness of the proposed method is verified by numerical simulation and experimental tests.