Analysis of the Impact of Lubrication on the Dynamic Behavior of Ball Bearings Using Artificial Neural Networks

被引：0

作者：

Knezevic, Ivan ^{[1
]}

Zivkovic, Aleksandar ^{[1
]}

Rackov, Milan ^{[1
]}

Kanovic, Zeljko ^{[1
]}

Bojanic Sejat, Mirjana ^{[1
]}

机构：

[1] Fac Tech Sci, Trg Dositeja Obradov 6, Novi Sad 21000, Serbia

来源：

ROMANIAN JOURNAL OF ACOUSTICS AND VIBRATION | 2019年 / 16卷 / 02期

关键词：

ball bearing; artificial neural network; lubrication; amplitude of vibration speed; DIAGNOSIS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Ball bearings can be found in all machines with rotary movement. Dynamic behavior analysis of ball bearings is a phenomenon that has been studied for many years. Previous studies on the dynamic behavior of ball bearings are based on describing the state of ball bearings using mathematical models. In addition to mathematical description, computers simulations based on finite element methods are also applied. This paper presents study of the dynamic behavior of ball bearings using artificial neural networks. Artificial neural network models are able to predict the amplitude of vibration that will generate the analyzed bearings depending on the lubrication. In this way, by the application of artificial neural networks bearing dynamic behavior can be predicted depending on the geometric parameters and lubrication.

引用

页码：178 / 183

页数：6

共 8 条

[1]

Harris T., 2001, ROLLING BEARING ANAL

[2] Rolling element bearing fault diagnosis using convolutional neural network and vibration image [J].

Hoang, Duy-Tang ;

Kang, Hee-Jun .

COGNITIVE SYSTEMS RESEARCH, 2019, 53 :42-50

[3]

Hong S., 2018, REMAINING USEFUL LIF

[4]

Ilic V., 2016, MEASURING SYSTEM CON

[5] Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms [J].

Jack, LB ;

Nandi, AK .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2002, 16 (2-3) :373-390

[6] Fault diagnosis of ball bearings using machine learning methods [J].

Kankar, P. K. ;

Sharma, Satish C. ;

Harsha, S. P. .

EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (03) :1876-1886

[7] Electric motor faults diagnosis using artificial neural networks [J].

Li, LX ;

Mechefske, CK ;

Li, WD .

INSIGHT, 2004, 46 (10) :616-621

[8] On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research [J].

Sick, B .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2002, 16 (04) :487-546

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