Bogie Fault Identification Based on EEMD Information Entropy and Manifold Learning

被引：11

作者：

Qin, Na ^{[1
]}

Sun, Yongkui ^{[1
]}

Gu, Pengju ^{[1
]}

Ma, Lei ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

关键词：

High speed train; fault recognition; empirical mode decomposition information entropy; feature extraction; manifold learning; EMPIRICAL MODE DECOMPOSITION; DIAGNOSIS;

D O I：

10.1016/j.ifacol.2017.08.052

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to realize high-speed train bogie's fault intelligent identification by data driven method, this paper proposes a new fault diagnosis framework. The main idea of the framework is to use features of ensemble empirical mode decomposition entropy, to reduce the feature dimension by Isometric Feature Mapping Manifold Learning, and identify the faults using support vector machine. The proposed method increases the fault detection rate effectively. Experimental results verify that the new method increases the accuracy of fault detection rate of the bogie failure. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：315 / 318

页数：4

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