轴承故障诊断中特征选取技术

Research onfeature selection technology in bearing fault diagnosis

针对轴承故障诊断建模中如何通过筛选有效特征提高模型诊断准确率的问题,提出一种新的特征选取方法。在计算所得特征集合中,利用诊断模型直接对特征进行判断,将高于阈值的诊断准确率对应的特征(组合)选取为显著特征,以显著特征导向选取方式,找到候选特征集合中维度低、诊断准确率高的特征。试验结果表明,本研究提出的方法可筛选出有效特征,降低模型参数、减少样本需求量、提高模型准确率,提升了故障诊断的效率。

A new method based on feature selection(FS) was proposed to select efficient features to promote the classification accuracy in bearing fault diagnosis. First, the outstanding features whose classification accuracy were higher than the threshold were directly selected by diagnosis model from a big feature set. Then the significant combinations of features which had less dimensions and higher classification accuracy were selected in the candidate feature set by a distinctive feature-oriented manner. Experiments showed that the proposed method had advantages in selecting efficient features, reducing the model parameters, decreasing the demand of samples and enhancing the model classification accuracy. As a result, it provided a new idea for feature selection and improved the efficiency of bearing fault diagnosis.