To read this content please select one of the options below:

An artificial neural network application to fault detection of a rotor bearing system

Hamdi Taplak (Vocational School of Kayseri, Erciyes University, Kayseri, Turkey)
İbrahim Uzmay (Department of Mechanical Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey)
Şahin Yıldırım (Department of Mechanical Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 1 January 2006

931

Abstract

Purpose

To improve the application neural networks predictors on bearing systems and to investigate the exact neural model of the ball‐bearing system.

Design/methodology/approach

A feed forward neural network is designed to model‐bearing system. Two results are compared for finding the exact model of the system.

Findings

The results of the proposed neural network predictor gives superior performance for analysing the behaviour of ball bearing undergoing loading deformation.

Research limitations/implications

The results of the proposed neural network exactly follows desired results of the system. Neural network predictor can be employed in practical applications.

Practical implications

As theoretical and practical study is evaluated together, it is hoped that ball‐bearing designers and researchers will obtain significant results in this area.

Originality/value

This paper fulfils an identified research results need and offers practical investigation for an academic career and research. Also, It should be very helpful for industrial application of ball‐bearing systems.

Keywords

Citation

Taplak, H., Uzmay, İ. and Yıldırım, Ş. (2006), "An artificial neural network application to fault detection of a rotor bearing system", Industrial Lubrication and Tribology, Vol. 58 No. 1, pp. 32-44. https://doi.org/10.1108/00368790610640082

Publisher

:

Emerald Group Publishing Limited

Copyright © 2006, Emerald Group Publishing Limited

Related articles