Synchronization in arrays of coupled self-induced friction oscillators

Marszal, Michał; Saha, Ashesh; Jankowski, Krzysztof; Stefański, Andrzej

doi:10.1140/epjst/e2016-60007-1

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…However, most of these mechanisms are limited to single-point contacts in idealized model systems. Spatially distributed contacts are well-known for adding aspects of synchronization [81], multistability [82,83] and localization [84,85], i.e. complicating the picture of stability drastically.…”

Section: Input-output Behavior Of Dynamical Systemsmentioning

confidence: 99%

Deep learning for brake squeal: vibration detection, characterization and prediction

Stender,

Tiedemann,

Spieler

et al. 2020

Preprint

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Despite significant advances in numerical modeling of brake squeal, the majority of industrial research and design is still conducted experimentally. In this work we report on novel strategies for handling data-intensive vibration testings and gaining better insights into brake system vibrations. To this end, we propose machine learning-based methods to detect and characterize vibrations, understand sensitivities and predict brake squeal. Our aim is to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical sounds in vibration recordings. The detection method is rooted in recent computer vision techniques for object detection. It allows to overcome limitations of classical approaches that rely on spectral properties of the recorded vibrations. Results indicate superior detection and characterization quality when compared to state-of-the-art brake squeal detectors. In the second part, deep recurrent neural networks are employed to learn the parametric patterns that determine the dynamic stability of the brake system during operation. Given a set of multivariate loading conditions, the models learn to predict the vibrational behavior of the structure. The validated models represent virtual twins for the squeal behavior of a specific brake system. It is found that those models can predict the occurrence and onset of brake squeal with high accuracy. Hence, the deep learning models can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability. Large data sets from commercial brake system testing are used to train and validate the deep learning models.

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Section: Input-output Behavior Of Dynamical Systemsmentioning

confidence: 99%

Deep learning for brake squeal: vibration detection, characterization and prediction

Stender,

Tiedemann,

Spieler

et al. 2020

Preprint

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show abstract

“…More recently, alternative criteria for local stability of the synchronization manifold were presented in [14,15], where synchronization of limit cycles in piecewise-linear models of neurons was investigated through the application of the MSF technique, exploiting the construction of appropriate Poincaré maps for the system of interest. Local synchronizability was also the main focus of the work on coupled dryfriction oscillators presented in [41,42].…”

Section: Brief Overview Of the Previous Literaturementioning

confidence: 99%

Convergence and Synchronization in Networks of Piecewise-Smooth Systems via Distributed Discontinuous Coupling

Coraggio¹,

DeLellis²,

Bernardo³

2019

Preprint

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Piecewise-smooth systems are common in applications, ranging from dry friction oscillators in mechanics, to power converters in electrical engineering, to neuron cells in biology. While the properties of stability and the control of such dynamical systems have been studied extensively, the conditions that trigger specific collective dynamics when many of such systems are interconnected in a network are not fully understood. The study of emergent behaviour, and in particular synchronization, has applicability in seismology, for what concerns the dynamics of neighbouring faults, in determining load balancing in power grids, and more. To enforce asymptotic state synchronization, we propose the addition of a discontinuous coupling action to the commonly used diffusive coupling term; even with the possibility that the two coupling protocols are associated to different graph topologies. This allows that convergence is achieved regardless of initial conditions, and without the use of any centralised control action. Moreover, we show that the minimum threshold on the coupling gain associated to the new discontinuous coupling protocol depends on the density of the sparsest cut in the graph. Namely, this crucial quantity, which we called minimum density, plays a role very similar to that of the algebraic connectivity in the case of networks of smooth systems, in describing the relation between synchronizability and topology.

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“…In [9], an extension of the Master Stability Function (MSF) approach to networks of PWS oscillators is presented, under some restrictive assumptions, obtaining a condition on the coupling gain to ensure local stability of the synchronous solution. Similarly, the MSF method is applied to dry friction oscillators in [10], [11]. Furthermore, sufficient conditions were found in [12] for controlling coupled PWS chaotic systems towards a desired solution, provided that a discontinuous control action is added to every node in the network.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Networks of Piecewise-Smooth Systems

Coraggio

DeLellis

Hogan

et al. 2018

IEEE Control Syst. Lett.

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We study convergence in networks of piecewisesmooth systems that commonly arise in applications to model dynamical systems whose evolution is affected by macroscopic events such as switches and impacts. Existing approaches were typically oriented towards guaranteeing global bounded synchronizability, local stability of the synchronization manifold, or achieving synchronization by exerting a control action on each node. Here we start by generalising existing results on QUAD (quadratic) systems to the case of piecewise-smooth systems, accounting for a large variety of nonlinear coupling laws. Then, we propose that a discontinuous coupling can be used to guarantee global synchronizability of a network of N piecewise-smooth agents under mild assumptions on the individual dynamics. We provide extensive numerical simulations to gain insights on larger networks.

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Synchronization in arrays of coupled self-induced friction oscillators

Cited by 10 publications

References 20 publications

Deep learning for brake squeal: vibration detection, characterization and prediction

Deep learning for brake squeal: vibration detection, characterization and prediction

Convergence and Synchronization in Networks of Piecewise-Smooth Systems via Distributed Discontinuous Coupling

Synchronization of Networks of Piecewise-Smooth Systems

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