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A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks.
- Source :
-
Mechanical Systems & Signal Processing . Jan2017, Vol. 83, p210-227. 18p. - Publication Year :
- 2017
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08883270
- Volume :
- 83
- Database :
- Academic Search Index
- Journal :
- Mechanical Systems & Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 118030066
- Full Text :
- https://doi.org/10.1016/j.ymssp.2016.06.008