Neural Network based estimation of friction coefficient of wheel and rail

Tibor Gajdar, Imre Rudas, Yoshihiro Suda

Research output: Contribution to conferencePaper

18 Citations (Scopus)

Abstract

The number of modern control theory applications in vehicle dynamics are emerging and have led to great progress in vehicle stability, handling and ride comfort. However, some of the parameters needed for control applications are difficult to measure on line. Such examples are the wheel/rail contact forces, attack angles of wheelsets and the friction coefficient μ between wheel and rail of railway vehicles. Other areas where the adequate knowledge of adhesion is vital are the electric drive and adhesion control systems of locomotive drive systems, since as the result of changing friction coefficient wheel spinning, slipping can occur, which can cause faulty operation and overloading of traction units. In order to cope with this problem, this paper presents different methods to estimate the friction coefficient μ, based on Neural network estimation and a computational method.

Original languageEnglish
Pages315-318
Number of pages4
Publication statusPublished - Dec 1 1997
EventProceedings of the 1997 International Conference on Intelligent Engineering Systems, INES - Budapest, Hungary
Duration: Sep 15 1997Sep 17 1997

Other

OtherProceedings of the 1997 International Conference on Intelligent Engineering Systems, INES
CityBudapest, Hungary
Period9/15/979/17/97

ASJC Scopus subject areas

  • Engineering(all)
  • Materials Science(all)

Fingerprint Dive into the research topics of 'Neural Network based estimation of friction coefficient of wheel and rail'. Together they form a unique fingerprint.

  • Cite this

    Gajdar, T., Rudas, I., & Suda, Y. (1997). Neural Network based estimation of friction coefficient of wheel and rail. 315-318. Paper presented at Proceedings of the 1997 International Conference on Intelligent Engineering Systems, INES, Budapest, Hungary, .