基于绕封模型的故障特征选择方法研究

Research on Mechanical Fault Feature Selection Based on Wrapper Model

在机械故障诊断中,基于原始大特征量的故障状态识别会导致识别精度的下降。特征选择可以去除原始特征中的冗余特征,提高诊断精度。但以前广泛应用的基于过滤模型的特征选择方法不能满足进一步提高精度的要求。针对此问题,提出使用基于绕封模型的故障特征选择方法,它采用遗传算法对特征集寻优,样本划分法进行错误率预测估计和BP神经网络学习算法进行分类。轴承诊断实例证明,此方法有较好的寻优特征子集的能力,可以提高系统的诊断精度。

Applying lots of primal features to identify fault condition leads to reduce classification correctness. Feature selection can remove redundant features in the primal features to enhance the effect of diagnosis. Filter method which was widely applied before isn＇t satisfied with the further demand for diagnosis correctness. A feature selection method based on wrapper model was proposed. The approaches to the subject include： genetic algorithm for optimal feature subset selection; k-fold cross-validation method for error rate evaluation and BP neural network for fault classification. As an example, the results of bearing fault diagnosis prove that the method possesses excellent optimization feature subset property, and obtains high correctness rate.