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Title: Intelligent approaches to modelling and interpreting disc brake squeal data
Author: Feraday, Simon Andrew
ISNI:       0000 0001 3459 9136
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2000
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Brake squeal is a complex and fugitive phenomenon which conventional methods of dynamic modelling struggle to predict accurately. This is due in part to the complex (and indeed changing) topology of the components involved, partly to the different conditions of braking encountered in practice and partly also the innate difficulty of modelling a sliding friction interface. 'Black box', data based approaches are proposed here not necessarily as an alternative, but ideally for use alongside conventional (typically finite element) modelling techniques to help design braking systems with less propensity to squeal and thus reduce the considerable cost of warranty claims presently incurred by manufacturers. A direct neurofuzzy modelling approach is first examined in which overall squeal performance is related to parameters representing the brake design. Some qualitative design guidelines for reducing squeal occurrence are obtained, however it is evident that in such a form the technique is limited to little more than this. Consequently, a new method is developed which combines the 'black box' and dynamic modelling approaches, reconciling experimental data with mathematical models. The new 'parametric reconciliation' technique, besides predicting oscillatory frequencies and amplitudes also provides an element of understanding of an unknown system based purely on experimental data. Following detailed analysis the technique is validated using synthetic data before being applied to data from a brake test rig. A number of other original contributions are presented in support of this work including the 'singular value entropy' algorithm for quantifying the degree of correlation between a set of input vectors. Likewise, the theoretical poles of least squares AR models of noisy signals are also studied in some detail in order to understand the system models produced in the latter part of the thesis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Friction