Journal ArticleDOI
Nonlinear transient and chaotic interactions in disc brake squeal
Reads0
Chats0
TLDR
In this paper, the authors applied the complex eigenvalue analysis, the direct steady-state analysis and the transient nonlinear time domain analysis to an isotropic pad-on-disc finite element model representing a simple model of a brake system.About:
This article is published in Journal of Sound and Vibration.The article was published on 2015-04-28. It has received 66 citations till now. The article focuses on the topics: Disc brake & Intermittency.read more
Citations
More filters
Book ChapterDOI
Towards Overcoming the Challenges of the Prediction of Brake Squeal Propensity
TL;DR: In this paper, a linear complex eigenvalue analysis (CEA) combined with noise dynamometer tests has been used to predict brake squeal propensity, which is a fugitive nonlinear self-excitation phenomenon induced by friction.
References
More filters
Book
Chaos in dynamical systems
TL;DR: In the new edition of this classic textbook, the most important change is the addition of a completely new chapter on control and synchronization of chaos as mentioned in this paper, which will be of interest to advanced undergraduates and graduate students in science, engineering and mathematics taking courses in chaotic dynamics, as well as to researchers in the subject.
Journal ArticleDOI
Recurrence plots for the analysis of complex systems
TL;DR: The aim of this work is to provide the readers with the know how for the application of recurrence plot based methods in their own field of research, and detail the analysis of data and indicate possible difficulties and pitfalls.
Nonlinear Time Series Analysis
Holger Kantz,Thomas Schreiber +1 more
TL;DR: In this article, the authors discuss the use of non-linear methods when determinism is weak and apply them to the problem of neighbor search in the presence of chaotic data and noise.
Book
Analysis of Observed Chaotic Data
TL;DR: Regular Dynamics: Newton to Poincare KAM Theorem, and the Chaos Toolkit: Making 'Physics' out of Chaos.
Nonlinear Time Series Analysis.
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.