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首页> 外文期刊>IEEE Transactions on Industry Applications >Fault Diagnosis of Bearing Damage by Means of the Linear Discriminant Analysis of Stator Current Features From the Frequency Selection
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Fault Diagnosis of Bearing Damage by Means of the Linear Discriminant Analysis of Stator Current Features From the Frequency Selection

机译：基于频率选择的定子电流特性线性判别分析的轴承损伤故障诊断

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摘要

Bearing damage is the most common failure in electrical machines. It can be detected by vibration analysis. However, this diagnosis method is costly or not always accessible due to the location of the equipment and the choice of the implemented sensors. An alternative method is provided with the electrical monitoring using the stator current of the electrical machine. This study aims at developing a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum. The features are evaluated by means of the linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier. The proposed method is evaluated by two types of damages at different load cases. The results show that the damaged bearings can be distinguished from the healthy bearing depending on the considered load cases.
机译：轴承损坏是电机中最常见的故障。可以通过振动分析来检测。但是，由于设备的位置和所采用的传感器的选择，这种诊断方法成本高昂或无法始终获得。提供了一种替代方法，该方法使用电机的定子电流进行电气监控。这项研究旨在开发一种基于定子电流频谱中频率选择所产生的电流特征的诊断系统。通过线性判别分析评估特征，并使用贝叶斯分类器执行故障诊断。所提出的方法通过在不同载荷工况下的两种类型的破坏进行评估。结果表明，根据考虑的载荷工况，可以将受损轴承与正常轴承区分开。

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