【摘要】 滚动轴承振动信号是非线性、非平稳信号,如何对复杂的非周期滚动轴承数据进行准确特征提取十分具有挑战性.本文提出一种基于局部频谱的轴承数据特征提取方法.该方法将预处理得到的分割点与频谱分析结合起来,构建了数据的局部化特征,确定了局部频率的定义以及时频域的构造方法,并对局部频谱进行特征提取.实验表明,该方法克服了希尔伯特变换仅适合描述窄带信号的局限性,并弥补傅里叶全局频率只对无限波动周期信号才具有明显价值的缺陷.减少虚假频率产生的同时,兼容了时域和频域的分析能力,为非线性非平稳滚动轴承时域数据的特征提取提供了一种新方法,在滚动轴承故障诊断方面有很高的实用价值.更多还原

【Abstract】 The vibration signal of rolling bearing is a nonlinear and unstable signal. Therefore it is very challenging to carry out feature extraction accurately from the complicated data of non-periodic rolling bearing. This article hereby proposes a method of feature extraction based on local spectrum bearing data. This method combined the segmentation point obtained from pretreatment and the spectrum analysis,built localized feature of the data,determined the definition of the local frequency and the construction method of time-frequency domain,and implemented the feature extraction. Experiments show that this method overcame the limitation that Hilbert transform is only suitable to describe the narrowband signals. It also made up for the defects of Fourier global frequency which is only valuable to the infinite wave period signals. As a new method of feature extraction from the time domain data of the nonlinear and unstable rolling bearing, it reduces the false frequency and is compatible with the analysis of both time domain and frequency domain. It has very high practical value in the fault diagnosis of rolling bearings.更多还原