Damage detection and assessment by using Higher Order Statistic Analysis has proved several times to be doable and particularly reliable; indeed, Fourier transform of displacements’ third-order cumulants, also known as Bispectrum, has the advantage of being able to detect non-linearity in the dynamic response of the structural element, while being insensitive to ambient vibrations and Gaussian noise. Thus, asymmetry in the statistic distribution may be easily spotted and related to the damaged conditions, as the majority of common faults, e.g. fatigue cracks, shows bilinear effects. In this study, firstly a novel approach to damage localisation, resorting to Neural Networks fed with bispectral data, is presented. Afterwards, NNs’ parameters and architecture, as well as several different selections of input data, are investigated in order to maximise its forecast abilities. To validate the introduced approach, a simple finite element model of a 4-meterslong cantilever beam has been built and data have been generated via FE nonlinear analyses performed on it. This model is intended to be a first concept, as generic as possible, of various beam-like structural elements.

Using bispectral analysis and neural networks to localise cracks in beam-like structures / Civera, Marco; Zanotti Fragonara, Luca; Surace, Cecilia. - ELETTRONICO. - 2:(2016), pp. 1542-1551. (Intervento presentato al convegno 8th European Workshop on Structural Health Monitoring, EWSHM 2016 tenutosi a Bilbao (Spain) nel 2016).

Using bispectral analysis and neural networks to localise cracks in beam-like structures

CIVERA, MARCO;SURACE, Cecilia
2016

Abstract

Damage detection and assessment by using Higher Order Statistic Analysis has proved several times to be doable and particularly reliable; indeed, Fourier transform of displacements’ third-order cumulants, also known as Bispectrum, has the advantage of being able to detect non-linearity in the dynamic response of the structural element, while being insensitive to ambient vibrations and Gaussian noise. Thus, asymmetry in the statistic distribution may be easily spotted and related to the damaged conditions, as the majority of common faults, e.g. fatigue cracks, shows bilinear effects. In this study, firstly a novel approach to damage localisation, resorting to Neural Networks fed with bispectral data, is presented. Afterwards, NNs’ parameters and architecture, as well as several different selections of input data, are investigated in order to maximise its forecast abilities. To validate the introduced approach, a simple finite element model of a 4-meterslong cantilever beam has been built and data have been generated via FE nonlinear analyses performed on it. This model is intended to be a first concept, as generic as possible, of various beam-like structural elements.
2016
9781510827936
9781510827936
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2658387
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