一类滚动轴承振动信号特征提取与模式识别

复杂工况下滚动轴承振动信号通常表现出强烈的非平稳性,而一些典型的故障特征往往容易被其他成分所掩盖,这为故障特征提取带来了很大的困难。针对这一问题,首先,提出一种基于同步压缩小波变换的滚动轴承信号特征提取方法,对多种工况下的滚动轴承振动信号进行分析,提取出能够有效反映滚动轴承工况的信号特征空间;其次,采用非负矩阵分解对信号特征空间进行精简和优化,提炼出用于滚动轴承故障诊断和模式识别的特征参数;最后,采用支持向量机对多种工况的滚动轴承振动信号进行分类。研究结果表明,与传统的时域特征参数提取方法相比,所提出的方法具有更高的分类准确率。

Feature Extraction and Pattern Recognition of Vibration Signals in a Rolling Bearing

The vibration signal of the rolling bearing is usually nonstationary under complicated operating status and some typical fault features tend to be covered by other components, which brings great difficulty for the fault feature extraction. In the light of this problem, a new procedure based on the synchrosqueezed wavelet transform (SWT) is proposed for the feature extraction of the rolling bearing signal. The vibration signals of the rolling bearing are analyzed under various operating status and the signal feature space is extracted to reflect the operating conditions of rolling bearing. Second, the non-negative matrix factorization (NMF) is performed to simplify and optimize the signal feature space so as to extract the feature parameters for fault diagnosis and pattern recognition. Finally, the support vector machine is applied to classify the various vibration signals of the rolling bearing. The results indicate that the proposed method is superior to the traditional time domain feature extraction method in pattern recognition.