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首页> 外文期刊>Mechanical systems and signal processing >Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing
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Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing

机译：使用决策树进行特征选择并通过近邻支持向量机进行分类，以进行滚动轴承的故障诊断

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摘要

Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing's nature of vibration reveals its condition and the features that show the nature, are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Often the problem is, finding out good features that discriminate the different fault conditions of the bearing. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper illustrates the use of a Decision Tree that identifies the best features from a given set of samples for the purpose of classification. It uses Proximal Support Vector Machine (PSVM), which has the capability to efficiently classify the faults using statistical features. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted therefrom and classified successfully using PSVM and SVM. The results of PSVM and SVM are compared.
机译：滚动轴承是旋转机械中使用最广泛的旋转元件之一。滚动轴承的振动性质揭示了其状况，而显示该性质的特征则应通过一些间接手段来提取。统计参数（例如峰度，标准偏差，最大值等）形成一组功能，这些功能广泛用于故障诊断中。通常的问题是，找出可以区分轴承不同故障状况的良好特征。良好特征的选择是模式识别的重要阶段，需要详细的领域知识。本文说明了决策树的用途，该决策树从给定的样本集中识别出最佳特征以进行分类。它使用近邻支持向量机（PSVM），它具有使用统计功能对故障进行有效分类的能力。在以下情况下会捕获来自压电换能器的振动信号：良好的轴承，有内圈故障的轴承，有外圈故障的轴承以及内外圈故障。从中提取统计特征，并使用PSVM和SVM成功分类。比较了PSVM和SVM的结果。

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