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The Use of AI Methods for Evaluating Condition Dependent Dynamic Models of Vehicle Brake Squeal

  • Simon Feraday
  • Chris Harris
  • Kihong Shin
  • Mike Brennan
  • Malcolm Lindsay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)

Abstract

A neurofuzzy modelling technique is used to predict the differential equation coefficients of brake noise time histories as functions of braking test conditions. These are then related to the 3rd order differential equations governing a candidate mathematical model of brake squeal using a second neurofuzzy model. This determines whether similar or sensible parametric changes in the model are required to mirror the dynamic effects of changes in experimental condition parameters. An assessment of the efficacy of the candidate model is then made based on this analysis. The results of different candidate models could be likewise compared to determine which is most realistic.

Keywords

Candidate Model Disc Temperature Model Parameter Space Stop Test Brake Squeal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Simon Feraday
    • 1
  • Chris Harris
    • 1
  • Kihong Shin
    • 2
  • Mike Brennan
    • 3
  • Malcolm Lindsay
    • 4
  1. 1.Image, Speech and Intelligent Systems Research GroupUniversity of SouthamptonSouthamptonUK
  2. 2.School of Mechanical EngineeringHanyang UniversitySeoulSouth Korea
  3. 3.Institute of Sound and Vibration ResearchUniversity of SouthamptonSouthamptonUK
  4. 4.TRW Braking SystemsOldwych Lane East KenilworthWarwickshireUK

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