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Anzin-Saint-Aubin, France

Nechak L.,MIPS Laboratory | Berger S.,MIPS Laboratory | Aubry E.,MIPS Laboratory
Journal of Computational and Nonlinear Dynamics | Year: 2014

This paper is devoted to the robust modeling and prediction of limit cycle oscillations in nonlinear dynamic friction systems with a random friction coefficient. In recent studies, the Wiener-Askey and Wiener-Haar expansions have been proposed to deal with these problems with great efficiency. In these studies, the random dispersion of the friction coefficient is always considered within intervals near the Hopf bifurcation point. However, it is well known that friction induced vibrations - with respect to the distance of the friction dispersion interval to the Hopf bifurcation point - have different properties in terms of tansient, frequency and amplitudes. So, the main objective of this study is to analyze the capabilities of the Wiener-Askey (general polynomial chaos, multielement generalized polynomial chaos) and Wiener-Haar expansions to be efficient in the modeling and prediction of limit cycle oscillations independently of the location of the instability zone with respect to the Hopf bifurcation point. © 2014 by ASME. Source

Grigoryev D.,MIPS Laboratory | Grigoryev D.,Goodyear Innovation Center | Lauffenburger J.-P.,MIPS Laboratory | Caroux J.,Goodyear Innovation Center | And 2 more authors.
IECON Proceedings (Industrial Electronics Conference) | Year: 2011

In order to detect specific steering maneuvers in closed-loop driving evaluations on open road, a novel pattern recognition approach is proposed. Based on signature analysis, this approach incorporates both signal processing techniques and expert knowledge. Key properties of this approach are the ability to adapt to the complexity of patterns it recognizes and its good sensitivity in distinguishing similar patterns. The objective of this paper is two-fold. First, a novel pattern recognition algorithm is introduced, and its performance is assessed in terms of "miss" and "false positive" rates. Second, different optimization algorithms were compared, which were used to improve recognition accuracy by finding an optimal set of classification features. This paper describes the approach which is suitable to recognize a wide range of patterns in different classes of signals. The approach was validated in the domain of automotive engineering, but it is generic enough to be applied to other domains where instrumented tests and measurements are commonplace. © 2011 IEEE. Source

Nechak L.,MIPS Laboratory | Berger S.,MIPS Laboratory | Aubry E.,MIPS Laboratory
International Multi-Conference on Systems, Signals and Devices, SSD'11 - Summary Proceedings | Year: 2011

This paper is devoted to the stability analysis of uncertain nonlinear dynamic systems. The generalized polynomial chaos formalism is proposed to deal with this challenging problem treated in most cases by using the prohibitive Monte Carlo based techniques. Two equivalent methods combining the non-intrusive generalized polynomial chaos with the indirect Lyapunov method are presented. Both methods are shown to be efficient in the estimation of the stability and instability regions of nonlinear dynamic systems with probabilistic uncertainties. Indeed, it is illustrated that the proposed methods give results of high accuracy and high confidence levels at lower cost compared with the classic Monte Carlo based method. © 2011 IEEE. Source

Nechak L.,MIPS Laboratory | Berger S.,MIPS Laboratory | Aubry E.,MIPS Laboratory
Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME | Year: 2012

This paper deals with the modeling and the prediction of the dynamic behavior of uncertain nonlinear systems. An efficient method is proposed to treat these problems. It is based on the Wiener-Haar chaos concept resulting from the polynomial chaos theory and it generalizes the use of the multiresolution analysis well known in the signal processing theory. The method provides a powerful tool to describe stochastic processes as series of orthonormal piecewise functions whose weighting coefficients are identified using the Mallat pyramidal algorithm. This paper shows that the Wiener-Haar model allows an efficient description and prediction of the dynamic behavior of nonlinear systems with probabilistic uncertainty in parameters. Its contribution, compared to the representation using the generalized polynomial chaos model, is illustrated by evaluating the two models via their application to the problems of the modeling and the prediction of the dynamic behavior of a self-excited uncertain nonlinear system. © 2012 by ASME. Source

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