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

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.