A 2D Hopfield Neural Network approach to mechanical beam damage detection

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Almeida, J (Autor) Outra; Alonso, H (Autor) Outra; Pedro Leal Ribeiro (Autor) FEUP; Rocha, P (Autor) FEUP

Revista

Título: Multidimensional Systems and Signal Processing

Vol. 26 Nº 4

Páginas: 1081-1095

ISSN: 0923-6082

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00G-P5T

DOI: 10.1007/s11045-015-0342-7

Abstract (EN): The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15