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首页> 外文会议>International Conference on Natural Computation;ICNC '09 >The Feature Selection in Rolling Bearing Fault Diagnosing Based on Parts-Principle Component Analysis
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The Feature Selection in Rolling Bearing Fault Diagnosing Based on Parts-Principle Component Analysis

机译：基于零件主成分分析的滚动轴承故障诊断中的特征选择

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摘要

In this study, PCA (Principal Component Analysis) was used to select features and eliminate the redundancy features in process of rolling bearing fault monitoring. And then a new method was mentioned out to optimize the feature space with P-PCA (Parts Principal Component Analysis), which needs to deal with the data of each fault categories with PCA firstly, and then reconstructed the feature space with parts principal components that were got previously. Then recognize the rolling bearing fault patterns based on artificial neural network. The result of experiment indicate that compared with the PCA, the P-PCA avoid the interfering between features which belong to different fault patterns. Which make new feature space contains more useful information, decline the training error rate of artificial neural network, and raise the speed and accuracy of fault pattern recognizing.
机译：在这项研究中，PCA（主成分分析）用于选择特征并消除滚动轴承故障监测过程中的冗余特征。然后提出了一种新的用零件主成分分析法对特征空间进行优化的方法，该方法首先需要用主成分分析法处理各个故障类别的数据，然后再用零件主成分重构特征空间。是以前得到的。然后基于人工神经网络识别滚动轴承的故障模式。实验结果表明，与PCA相比，P-PCA避免了属于不同故障模式的特征之间的干扰。这使得新的特征空间包含更多有用的信息，降低了人工神经网络的训练错误率，提高了故障模式识别的速度和准确性。

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