A 2D neural network approach for beam damage detection

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

J. Almeida (Author) FEUP; Hugo Alonso (Author) FEUP; P. Rocha (Author) FEUP

Conference proceedings International

Title: nDS 2013 - Proceedings of the 8th International Workshop on Multidimensional Systems

Pages: 139-143

8th International Workshop on Multidimensional Systems, nDS 2013

9 September 2013 through 11 September 2013

Indexing

Scopus - 0 Citations

Scientific classification

FOS: Engineering and technology

Other information

Authenticus ID: P-00S-ST1

Abstract (EN): The aim of this contribution is to propose a 2D Hopfield Neural Network for parameter identification and consequent damage detection in 2D processes. This method is applied for online damage detection in Euler-Bernoulli beams subjected to external forces. Damage is associated with significant change in the parameter values of the model. The 2D Hopfield Neural Network presented here is an extension of a 1D Hopfield Neural Network recently proposed in the literature. At each time instant, the network produces an estimate of the parameters at a certain beam point based on the previous estimates in the point itself and in its neighbours. The network tracks the change in the beam parameters and the results obtained are very satisfactory. © VDE VERLAG GMBH 2013.

Language: English

Type (Professor's evaluation): Scientific