基于EMD和优化K-均值聚类算法诊断滚动轴承故障

考虑到滚动轴承振动信号的非平稳特征和实际应用中典型故障样本不易获得等原因,而在实际应用中,故障程度识别和故障类型诊断一样重要,提出一种滚动轴承故障类型及故障程度识别方法。首先对原始振动信号进行EMD分解,对含故障特征的IMF(intrinsic mode function)分量进行信号重构,随后对重构信号进行Hilbert包络谱分析,在提取特征量的基础上,应用优化K-均值聚类算法进行故障类型和故障程度分类。实验结果表明:基于EMD和优化K-均值聚类的故障类型和故障程度识别算法,可将含不同故障类型的样本集,按故障类型进行正确分类;也可将含同种故障类型、不同故障程度的样本集,按故障程度进行正确分类。

Fault diagnosis of bearing based on empirical modedecomposition and K-means clustering

The vibration signal is nonstationary and the sample with typical fault is difficult to acquire. The severity of fault diagnosis is the same important to style diagnosis. This paper presented a novel fault diagnosis of bearings based on the characteristic fault frequency and K-means clustering. The reconstructed signal could be obtained by some set of IMF components of the vibration signal by EMD. It performed the Hilbert envelope analysis to reconstructed signal. From the power spectrum of Hilbert envelope signal, it could identify the amplitude of the characteristic fault frequency and its integer multiples, which was used to diagnose the style and severity of fault. The result demonstrates that the proposed method based on EMD and K-means clustering can recognize the style and severity of bearing fault.