A Neural network approach to damage detection in Euler-Bernoulli beams subjected to external forces

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

Authors

Juliana Almeida (Author) Other; Hugo Alonso (Author) FEUP; Paula Rocha (Author) FEUP

Conference proceedings International

Title: 2013 21ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED)

Pages: 100-103

21st Mediterranean Conference on Control and Automation (MED)

Platanias, GREECE, JUN 25-28, 2013

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 1 Citation

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-008-EXG

DOI: 10.1109/med.2013.6608705

Abstract (EN): The aim of this contribution is to present two methods for online damage detection in Euler-Bernoulli beams subjected to external forces. Both methods detect damage by tracking changes in the beam parameters. Here, this change is assumed to occur in time, but not in space; that is, it occurs at a certain time instant, being the same along the beam. The input to the methods consists of the beam vibration data collected at different points. The first method is based on the use of a single Hopfield neural network. At each time instant, this network produces an estimate of the beam parameters and this estimate is the same for all beam points. In turn, the second method combines several Hopfield neural networks. At each time instant, each network produces an initial estimate of the parameters at a certain beam point and the estimates of neighbouring points are then combined to produce a final estimate at each point.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4