Chapter 20. Measurement and Identification of Pre-Sliding Friction Dynamics

  1. Prof. Dr. Günter Radons5 and
  2. Prof. Dr.-Ing. Reimund Neugebauer6
  1. F. Al-Bender1,
  2. V. Lampaert1,
  3. S.D. Fassois2,
  4. D.C. Rizos2,
  5. K. Worden3,
  6. D. Engster4,
  7. A. Hornstein4 and
  8. U. Parlitz4

Published Online: 28 JAN 2005

DOI: 10.1002/3527602585.ch20

Nonlinear Dynamics of Production Systems

Nonlinear Dynamics of Production Systems

How to Cite

Al-Bender, F., Lampaert, V., Fassois, S.D., Rizos, D.C., Worden, K., Engster, D., Hornstein, A. and Parlitz, U. (2004) Measurement and Identification of Pre-Sliding Friction Dynamics, in Nonlinear Dynamics of Production Systems (eds G. Radons and R. Neugebauer), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, FRG. doi: 10.1002/3527602585.ch20

Editor Information

  1. 5

    Technische Universität Chemnitz, Institut für Physik, Theoretische Physik I, Komplexe Systeme und Nichtlineare Dynamik, Germany

  2. 6

    Fraunhofer Institut für Werkzeugmaschinen und Umformtechnik IWU Chemnitz, Germany

Author Information

  1. 1

    Catholic University of Leuven, Department of Mechanical Engineering, Belgium

  2. 2

    University of Patras, Department of Mechanical and Aeronautical Engineering, Greece

  3. 3

    University of Sheffield, Department of Mechanical Engineering, United Kingdom

  4. 4

    Universität Göttingen, III. Physikalisches Institut, Germany

Publication History

  1. Published Online: 28 JAN 2005
  2. Published Print: 28 JAN 2004

ISBN Information

Print ISBN: 9783527404308

Online ISBN: 9783527602582

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Keywords:

  • measurement;
  • identification;
  • pre-sliding friction dynamics;
  • NARMAX models;
  • physics-based models;
  • regression approaches

Summary

This chapter contains sections titled:

  • Introduction

  • Friction Characterization

    • Friction Model Structures

    • Acquisition of Friction Data

    • Simulation of Friction Data

  • Identification Methods and Results

  • Regression and Time-Series Modeling

    • NARMAX Models

    • Support Vector Models

    • Local Models

    • Neural Network Methods

    • Numerical Results of Black-box Methods

  • Identification of Physics-based Models

    • The Linear Regression (LR) Approach

    • The Dynamic Linear Regression (DLR) Approach

    • The Nonlinear Regression (NLR) Approach

    • Model Order Selection and Assessment

    • Identification Results

  • Discussion and Conclusions

  • Bibliography