With the rapid development and application of high-speed railway EMU, its safety and reliability have attracted wide attention. In order to estimate the health status of axle box bearing of high-speed railway EMU accurately (hereinafter referred to as axle box bearing), this study proposes a classification algorithm based on Decision Tree and Support Vector Machine, and utilizes Principal Component Analysis (PCA) to reduce the feature dimension simultaneously. In addition, the performance of the classification can be further improved by collecting the temperature data of axle box bearing and various components on the drive side and non-drive side of which either in the same bogie or in different bogie. And the Analytic Hierarchy Process (AHP) has been used to distribute the weight of vectors. Extensive experiments demonstrate the effectiveness of the classification model, and the accuracy has been increased by about 5%. Furthermore, the judgment ability of health status of axle box bearing and the precision of the operation and maintenance policy can be enhanced if we establish a health assessment model through dividing the health status of axle box bearing into four parts including health, temperature rising, strong temperature, and irritative temperature.%随着高速铁路动车组的快速发展和应用, 其安全性和可靠性引起了广泛关注. 为了准确判别高速铁路动车组轴箱轴承(以下简称轴箱轴承)的健康状态情况, 提出通过采集轴箱轴承温度及在相同和不同转向架驱动侧、非驱动侧各个部件的温度数据, 利用主成分分析法(PCA)进行特征降维, 将基于决策树的支持向量机(DT-SVM)多分类算法作为判别算法, 同时结合层次分析法(AHP)确定向量值权重, 从而进一步提高分类精度. 大量实验表明该方法可使分类准确率提升5%左右, 此外通过建立健康状态评估模型,将轴箱轴承健康状态分为健康、温升、强温和激温四类, 有助于提高轴箱轴承健康状态的判别力和运维决策的准确性.
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