滚动轴承故障特征选择的Filter与改进灰狼优化混合算法

Hybrid algorithm of filter and improved gray wolf optimization for fault feature selection of rolling bearing

为了从原始高维特征空间中选择最具鉴别能力的特征,提高轴承故障诊断精度,提出了一种Filter与改进灰狼优化混合的故障特征选择算法。首先,针对滚动轴承的原始振动信号,利用一种基于Hilbert-Huang变换的时频域特征提取策略建立高维敏感特征集合。然后,通过由ReliefF算法与拉普拉斯分数构成的混合Filter方法对原始特征集合进行相关性评估并快速筛选重要特征,从而完成特征集合的一次预选。最后,引入改进灰狼优化算法对预选特征集合进行二次筛选,实现冗余特征去除的同时,完成对支持向量机模型参数的优化。利用旋转机械振动试验台获取故障轴承数据进行了验证,试验结果表明,该方法显著提高了分类器模型的诊断准确率,有效实现了故障数据集的特征降维,并且与同类方法相比,所提方法具有更好的综合性能。

To select the most discriminating feature from the original high-dimensional feature space and improve the bearing fault diagnosis accuracy,a fault feature selection algorithm combining Filter and improved Gray Wolf optimization was proposed.For the original vibration signals of rolling bearings,a time-frequency domain feature extraction strategy based on Hilbert-Huang transform was used to establish a high-dimensional sensitive feature set.Then,a hybrid Filter method consisting of ReliefF and Laplacian Score(LS)was used to evaluate the relevance of the original feature set and quickly select important features.Thus,a pre-selection of feature set was completed.The Improved Grey Wolf Optimization(IGWO)was introduced to perform secondary selecting on the pre-selected feature set.At the same time,the parameters of support vector machine model were optimized.The experimental results showed that the proposed method significantly improved the diagnostic accuracy of the classifier model and effectively reduced the feature dimension of the fault data set.Compared with similar methods,the proposed method had better comprehensive performance.