A novel approach to damage localisation based on bispectral analysis and neural network

Show simple item record

dc.contributor.author Civera, M.
dc.contributor.author Zanotti Fragonara, Luca
dc.contributor.author Surace, C.
dc.date.accessioned 2018-02-08T15:02:53Z
dc.date.available 2018-02-08T15:02:53Z
dc.date.issued 2017-12-31
dc.identifier.citation M. Civera, L. Zanotti Fragonara, C. Surace. (2017) A novel approach to damage localisation based on bispectral analysis and neural network. Smart Structures and Systems, Volume 20, Issue 6, pp. 669-682 en_UK
dc.identifier.issn 1738-1584
dc.identifier.uri http://dx.doi.org/10.12989/sss.2017.20.6.669
dc.identifier.uri http://www.techno-press.org/?page=container&journal=sss&volume=20νm=6#
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/12970
dc.description.abstract The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation. en_UK
dc.language.iso en en_UK
dc.publisher Techno Press en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.subject structural health monitoring en_UK
dc.subject damage detection en_UK
dc.subject higher-order spectral analysis en_UK
dc.subject bispectrum en_UK
dc.subject neural network en_UK
dc.subject non-linear vibrations en_UK
dc.subject breathing crack en_UK
dc.title A novel approach to damage localisation based on bispectral analysis and neural network en_UK
dc.type Article en_UK

Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International

Search CERES


My Account