A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks
File(s)
Accepted version
Author(s)
Ondra, V
Sever, IA
Schwingshackl, CW
Type
Journal Article
Abstract
This paper presents a method for detection and characterization of structural non-linearities from a single frequency response function using the Hilbert transform in the frequency domain and arti cial 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 di erent 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 characterizing non-linear behaviour from a single frequency response function.
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 characterizing non-linear behaviour from a single frequency response function.
Date Issued
2017-01-15
Date Acceptance
2016-06-10
Citation
Mechanical Systems and Signal Processing, 2017, 83 (1), pp.210-227
ISSN
0888-3270
Publisher
Elsevier
Start Page
210
End Page
227
Journal / Book Title
Mechanical Systems and Signal Processing
Volume
83
Issue
1
Copyright Statement
© 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Rolls-Royce Plc
Identifier
https://www.sciencedirect.com/science/article/pii/S0888327016301911?via%3Dihub
Grant Number
P/O: 4600144010
Subjects
Science & Technology
Technology
Engineering, Mechanical
Engineering
Non-linear system characterisation
Hilbert transform
Neural network classification
Nonlinearity indexes
IDENTIFICATION
0905 Civil Engineering
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Acoustics
Publication Status
Published
Date Publish Online
2016-06-29