基于形态成分分析的轴承复合故障诊断

独立分量分析(ICA)已被广泛运用于线性混合模型的盲源分离问题,但却有2个重要的限制:信源统计独立和信源非高斯分布。形态成分分析是最新提出的一种基于稀疏表示的信号和图像分解(分离)方法。介绍了形态成分分析的基本原理,进行了仿真说明,并应用该方法对设置了3种故障的轴承进行故障诊断,最终发现了故障特征,成功判别出了轴承的3种故障,验证了该方法在轴承故障诊断中的有效性。

Compound Fault Diagnosis for Bearings Based on Morphological Component Analysis

The ICA is widely used in blind source separation of linear mixture model,which has two important limitations: statistically independent sources and non-Gauss distribution.The morphological component analysis(MCA) is a novel decomposition method based on sparse representation of signals and images.The basic principle of MCA is introduced and then illustrated with simulations,and the method is used to diagnose the bearing with three types of faults.Finally,the fault features are found out and the faults are distinguished successfully,which verify the validity of the method in fault diagnosis of bearing.