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Smart Structures and Systems
  Volume 20, Number 6, December 2017, pages 669-682

A novel approach to damage localisation based on bispectral analysis and neural network
M. Civera, L. Zanotti Fragonara and C. Surace

    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.
Key Words
    structural health monitoring; damage detection; higher-order spectral analysis; bispectrum; neural network; non-linear vibrations; breathing crack
M. Civera and C. Surace: Department of Structural, Building and Geotechnical engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy
Zanotti Fragonara: School of Aerospace, Transportation and Manufacturing, Cranfield University, College Road, Cranfield, MK43 0AL, United Kingdom


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