A Novel Method for Bearing Safety Detection in Urban Rail Transit Based on Deep Learning

被引：0

作者：

Tao, Jie ^{[1
,2
]}

Zhang, Shaobo ^{[1
]}

Yang, Dalian ^{[2
]}

机构：

[1] Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Peoples R China

[2] Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Peoples R China

来源：

SECURITY, PRIVACY, AND ANONYMITY IN COMPUTATION, COMMUNICATION, AND STORAGE (SPACCS 2018) | 2018年 / 11342卷

基金：

中国国家自然科学基金;

关键词：

Deep learning; Roller bearing; Empirical mode decomposition Safety detection; FAULT-DIAGNOSIS; NETWORK;

D O I：

10.1007/978-3-030-05345-1_42

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The double tapered roller bearing is widely used in urban rail transit, due to its complex structure, the traditional safety detection is difficult to recognize the early weak fault. In order to solve this problem, a deep learning method for safety detection of roller bearing is put forward. In the experiment, vibration signals of bearing are firstly separated into a series of intrinsic mode functions by empirical mode decomposition, then we extracted the transient energy to construct the eigenvectors. In the pattern recognition, deep learning method is used to generate the safety detector by unsupervised study. There are three states of rolling bearings in experiments, as normal, inner fault and outer fault. The results show that the proposed method is more stable and accurately to identify bearing faults, and the classification accuracy is above 98%.

引用

页码：485 / 496

页数：12