Open Access

Comparison of Stimuli for Nonlinear System Response Classification

  • Journal Article
  • 10-04-03-0014
  • ISSN: 2380-2162, e-ISSN: 2380-2170
Published February 13, 2020 by SAE International in United States
Comparison of Stimuli for Nonlinear System Response Classification
Sector:
Citation: Andersson, N. and Abrahamsson, T., "Comparison of Stimuli for Nonlinear System Response Classification," SAE Int. J. Veh. Dyn., Stab., and NVH 4(3):2020.
Language: English

Abstract:

As part of the development of an automated virtual design classification approach for nonlinear structural dynamics, alternative excitation functions are evaluated with respect to their overall performance and efficiency in feature-based response analysis. Robust design of nonlinear structures requires analysis of extensive parameter variations. Both the character of the stimulus and feature metrics used are central to the performance of a response classification approach. The main purpose of this study is to compare stimulus candidates with respect to their efficiency in response classification. A deterministic multilevel, multifrequency stepped-sine periodic test function is used as a baseline. Order-wise differences between generalized and linearized system frequency response functions are evaluated by a selected feature metric to allow categorization into primary, sub and super harmonic responses, as well as odd and even order response distortions. An alternative excitation function type is the pseudorandom phase multi-sine. Its robust variant estimates the best linear approximation of the generalized frequency response function and related nonlinear and noise variances, which can be used for response classification. The fast variant of this method further detects and classifies occurring even and odd order nonlinear responses using a hypothesis test. This article describes the application of these three methods to a virtually running two-piece rotor shaft model. Time response signals from simulated test parameter variations are used to calculate selected nonlinear feature metric values. The total simulation and measurement time, as well as the predictive performance in a few typical nonlinear response cases are evaluated.