Prediction of Squeal Noise Based on Multiresolution Signal Decomposition and Wavelet Representation—Application to FEM Brake Systems Subjected to Friction-Induced Vibration

This paper is devoted to discussion of the efficiency of reduced models based on a Double Modal Synthesis method that combines a classical modal reduction and a condensation at the frictional interfaces by computing a reduced complex mode basis, for the prediction of squeal noise of mechanical systems subjected to friction-induced vibration. More specifically, the use of the multiresolution signal decomposition of acoustic radiation and wavelet representation will be proposed to analyze details of a pattern on different observation scales ranging from the pixel to the size of the complete acoustic pattern. Based on this approach and the definition of specific resulting criteria, it is possible to quantify the differences in the representation of the acoustic fields for different reduced models and thus to perform convergence studies for different scales of representation in order to evaluate the potential of reduced models. The effectiveness of the proposed approach is tested on the finite element model of a simplified brake system that is composed of a disc and two pads. The contact is modeled by introducing contact elements at the two friction interfaces with the classical Coulomb law and a constant friction coefficient. It is demonstrated that the new proposed criteria, based on multiresolution signal decomposition, allow us to provide satisfactory results for the choice of an efficient reduced model for predicting acoustic radiation due to squeal noise.

MULTIRESOLUTION SIGNAL DECOMPOSITION AND APPROXIMATION BASED ON SUPPORT VECTOR MACHINES

The fusion of wavelet technique and support vector machines (SVMs) has become an intensive study in recent years. Considering that the wavelet technique is the theoretical foundation of multiresolution analysis (MRA), it is valuable for us to investigate the problem of whether a good performance could be obtained if we combine the MRA with SVMs for signal approximation. Based on the fact that the feature space of SVM and the scale subspace in MRA can be viewed as the same Reproducing Kernel Hilbert Spaces (RKHS), a new algorithm named multiresolution signal decomposition and approximation based on SVM is proposed. The proposed algorithm which approximates the signals hierarchically at different resolutions, possesses better approximation of smoothness for signal than conventional MRA due to using the approximation criterion of the SVM. Experiments illustrate that our algorithm has better approximation of performance than the MRA when being applied to stationary and non-stationary signals.