A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks

Vaclav Ondra, Ibrahim A. Sever, Christoph W. Schwingshackl

Research output: Contribution to journalArticle (Academic Journal)

Abstract

This paper presents a method for detection and characterisation of structural non-linearities from a single frequency response function using the Hilbert transform in the frequency domain and artificial neural networks. A frequency response function is described based on its Hilbert transform using several common and newly introduced scalar parameters, termed non-linearity indexes, to create training data of the artificial neural network. This network is subsequently used to detect the existence of non-linearity and classify its type. The theoretical background of the method is given and its usage is demonstrated on different numerical test cases created by single degree of freedom non-linear systems and a lumped parameter multi degree of freedom system with a geometric non-linearity. The method is also applied to several experimentally measured frequency response functions obtained from a cantilever beam with a clearance non-linearity and an under-platform damper experimental rig with a complex friction contact interface. It is shown that the method is a fast and noise-robust means of detecting and characterising non-linear behaviour from a single frequency response function.
Original languageEnglish
Pages (from-to)210-227
JournalMechanical Systems and Signal Processing
Volume83
Early online date29 Jun 2016
DOIs
Publication statusPublished - 2017

Keywords

  • Non-linear system characterisation
  • Hilbert transform
  • Neural network classification
  • Nonlinearity indexes

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