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

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Almeida, J (Author) Other; Alonso, H (Author) Other; Pedro Leal Ribeiro (Author) FEUP; Rocha, P (Author) FEUP

Journal

Title: Multidimensional Systems and Signal Processing

Vol. 26 No. 4

Pages: 1081-1095

ISSN: 0923-6082

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15