基于经验模态分解和形态滤波的矿用轴承故障诊断

Bearing Fault Diagnosis Based on Empirical Mode Decomposition and Morphology Filter

针对传统煤矿电机滚动轴承故障诊断信号噪声大和诊断效率低等问题,提出了一种基于经验模态分解和形态滤波的轴承故障诊断方法。仿真结果验证了所提方法的可行性和有效性。

For the problems of high noise and low efficiency in the bearing fault diagnosis in traditional coal mine motors, a fault diagnosis method based on empirical mode decomposition and morphological filter is proposed. Simulation results show the feasibility and effectiveness of the proposed method.