A Systematic Approach to Modal Testing of Nonlinear Structures

Carri, Arnaldo delli; Ewins, D. J.

doi:10.1007/978-1-4614-6585-0_25

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…For instance, the variation of conventional Frequency Response Functions (FRF) obtained for different constant excitation levels, and also differences in the reciprocity between pairs of driving point FRFs, can be judged as clear evidence of nonlinear behaviour. However, the localisation, characterisation and quantification of such nonlinear behaviour are still open research topics [3]. A tool capable of offering a better understanding of the behaviour of nonlinear systems is the backbone curve [2,4] which defines the natural frequency as a function of the amplitude of the system response when neither damping nor forcing are present.…”

Section: Introductionmentioning

confidence: 99%

Identification of backbone curves of nonlinear systems from resonance decay responses

Londoño

Neild

Cooper

2015

Journal of Sound and Vibration

128

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a b s t r a c tBackbone curves can offer valuable insight into the behaviour of nonlinear systems along with significant information about any coupling between the underlying linear modes in their response. This paper presents a technique for the extraction of backbone curves of lightly damped nonlinear systems that is well suited for the experimental investigation of structures exhibiting nonlinear behaviour. The approach is based on estimations of the instantaneous frequency and the envelope amplitude of a decaying response following a tuned steady-state oscillation of the system. Results obtained from simulations and experiments demonstrate that the proposed procedure is capable of achieving an accurate estimation of the backbone curves and damping ratios of the system provided that the premise of damping having low impact on its oscillation frequency is met.

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Section: Introductionmentioning

confidence: 99%

Identification of backbone curves of nonlinear systems from resonance decay responses

Londoño

Neild

Cooper

2015

Journal of Sound and Vibration

128

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“…The approaches are formularised in Eqs. (3) where the terms in bold and underlined are considered unknown to be found using the proposed method as described in the following section.…”

Section: Forced Vibration and Data Processingmentioning

confidence: 99%

“…Detection of nonlinearities can be achieved by simple techniques such as observation of distorted peaks of a FRF or recognition of jumps in time history responses [2]. However the localisation, characterization and quantification techniques applied to nonlinear system identification are still open research topics [3]. It is preferable to have a parsimonious model 1 capable of estimating dynamics of system accurately.…”

Section: Introductionmentioning

confidence: 99%

Direct optimisation based model selection and parameter estimation using time-domain data for identifying localised nonlinearities

Safari

Monsalve

2021

Journal of Sound and Vibration

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“…Although most of its applications are performed in the time domain, the Hilbert transform can also be applied in the frequency domain. Not only does the Hilbert transform provide a fast and effective means of detecting non-linear behaviour on the basis of a measured FRF [9,5], but it also provides limited insight into the type of non-linearities [2,1].…”

Section: Introductionmentioning

confidence: 99%

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

Ondra

Sever

Schwingshackl

2017

Mechanical Systems and Signal Processing

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a b s t r a c tThis 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 nonlinear 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.

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A Systematic Approach to Modal Testing of Nonlinear Structures

Cited by 8 publications

References 7 publications

Identification of backbone curves of nonlinear systems from resonance decay responses

Identification of backbone curves of nonlinear systems from resonance decay responses

Direct optimisation based model selection and parameter estimation using time-domain data for identifying localised nonlinearities

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

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