Nondestructive detection method for rolling element bearings by adaptive neural networks

被引：0

作者：

Wu, XJ ^{[1
]}

Li, CG ^{[1
]}

Lei, M ^{[1
]}

Yang, SZ ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Dept Mech, Wuhan 430074, Peoples R China

来源：

CONDITION MONITORING '97 | 1997年

关键词：

artificial neural networks; adaptive learning algorithm; fault diagnosis; rolling element bearing;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The amplitude and frequency; characteristics of vibration for a defective rolling element bearing are discussed. A nondestructive detection method for rolling element bearings is proposed, which is based an the adaptive neural networks. For improving its performance, an adaptive learning algorithm is proposed. The feasibility of the method is examined using some vibration signals of Type 2724 and Type 97726T bearings, which are used in the railway trains.

引用

页码：516 / 519

页数：4