基于EMD-ICA去噪的滚动轴承故障诊断方法

Fault diagnosis of rolling bearing based on EMD-ICA de-noising

针对轴承故障信号易受环境噪声影响、信噪分离难的问题,提出了一种基于经验模态分解和独立成分分析相结合去噪的滚动轴承故障诊断方法。给出了该方法在故障诊断信号去噪领域的应用原理、方法步骤和评价指标;并通过仿真信号和实际轴承的滚动体故障、内圈故障和外圈故障信号进行了分析和故障诊断,验证了该方法在轴承故障信噪分离中的有效性。结果表明,采用文中提出的方法消噪后提取故障信号特征频率,压制了噪声干扰,能明显区别出轴承的状态及其故障的类型,有效提高了轴承故障诊断的准确性。

A method of fault diagnosis was proposed which combined empirical mode decomposition with independent component analysis based on the facts that the fault signal of rolling bearings was affected easily by environment noise and difficult to be separated from noise.The principle and steps of the method were given and the de-noising effect was evaluated by some parameters.The simulated signal and actual fault signals of the bearing ball,the inner and outer race were analyzed and diagnosed.The results show that the proposed method can suppress the noise interference greatly.Fault characteristics extracted from the de-noised signal can distinguish the state of bearing and type of faults obviously.The accuracy of fault diagnosis of the bearing is improved effectively.