Detalhes bibliográficos
Ano de defesa: |
2014 |
Autor(a) principal: |
Lima, Fernando Parra dos Anjos [UNESP] |
Orientador(a): |
Não Informado pela instituição |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Universidade Estadual Paulista (Unesp)
|
Programa de Pós-Graduação: |
Não Informado pela instituição
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: |
|
Link de acesso: |
http://hdl.handle.net/11449/113857
|
Resumo: |
In this dissertation presents two methodologies to develop health monitoring of aircraft structures and mechanical systems, using intelligent computing techniques such as artificial neural networks and artificial immune systems. In this context, uses an ARTMAP-Fuzzy artificial neural network and the negative selection algorithm. Both techniques are used for the analysis, identification and characterization of structural failure due to the structure. The main application of these methods is to assist in the inspection of mechanical and aeronautical structures, to detect and characterize flaws as well, making decisions in order to avoid disasters/accidents. With these proposals one seeks to designing new systems for structural health monitoring that can be modified easily to cater to permanent evolution technologies and industry. To evaluate the proposed methodologies, experiments were performed in the laboratory to generate a database of captured signals in an aluminum beam. The results obtained by the methods are excellent, with robustness and accuracy |