摘要
针对故障特征维数过高导致故障的分类与辨识性能不佳的现状,提出一种基于中值特征线多图嵌入(median feature line multi-graph embedding,简称MFLME)的故障数据集降维算法.首先,将样本点到特征空间的投影度量改进为中值度量,削弱算法的外推误差;其次,通过定义近邻特征线图和远邻特征线图,减少异类样本的混淆,扩大类别间距,为后续故障的分类决策降低难度;最后,利用两个不同的转子故障模拟实验对算法性能进行验证.结果表明,该算法能降低故障分类难度,提升故障辨识准确率.
Abstract
To address the current situation that the fault feature dimensionality is too high,which leads to poor performance of fault classification and identification techniques,a fault dataset dimensionality reduction algo-rithm based on Median feature line multi-graph embedding(MFLME)is proposed.The algorithm improves the projection metric from sample points to feature space into the median metric in order to weaken the extrapolation error of the algorithm.Next,by defining the near-neighbor feature line graph and the far-neighbor feature line graph,the confusion of different kinds of samples is reduced and the category spacing is expanded,which re-duces the difficulty of the subsequent fault classification decision.Two different rotor failure simulation experi-ments are used to validate the algorithm performance.The results show that the algorithm can reduce the diffi-culty of fault classification and improve the identification accuracy.
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