Abstract: To better screen the fault features of the original high-dimensional vibration signals and improve the accuracy of rolling bearings fault diagnosis,a fault feature selection method based on weighted optimization of feature evaluation metrics was proposed.The smoothness priors approach was adaptively used to decompose the non-stationary vibration signals,and the various time-domain,frequency-domain and time-frequency domain features were extracted to construct an initial fault feature set.Then,four feature performance evaluation indexes of monotonicity,discrimination,identification and robustness were integrated,and a weighted linear combination based on the sine-cosine optimization algorithm was used to comprehensively evaluate the fault features performance,followed by the screening of sensitive fault features.The proposed method was applied to rolling bearings experimental data,and the support vector classifier was used as the diagnostic machine to verify the effectiveness of the proposed fault feature selection method.

Key words: rolling bearings, fault diagnosis, fault feature selection, fault feature evaluation

摘要： 为了更好地筛选原始高维振动信号的故障特征,提高滚动轴承故障诊断精度,提出一种特征评价指标加权优化的故障特征选择方法。首先采用平滑先验法自适应地分解轴承非平稳振动信号,并提取多种时域、频域和时频域特征构建初始故障特征集。然后,将单调性、区别性、识别性和鲁棒性4个特征性能评价指标融合,采用基于正余弦算法优化的加权线性组合综合评估故障特征性能,继而筛选出敏感故障特征。最后,将该方法应用于滚动轴承实验数据,采用支持向量分类机作为诊断器,验证所提出故障特征选择方法的有效性。

关键词: 滚动轴承, 故障诊断, 故障特征选择, 故障特征评价