日本機械学会論文集 C編
Online ISSN : 1884-8354
Print ISSN : 0387-5024
ニューラルネットワークと計算力学に基づくシステム同定の検討 : 第2報, 計測点の低減法に関する研究
施 勤忠萩原 一郎
著者情報
ジャーナル フリー

1998 年 64 巻 625 号 p. 3375-3382

詳細
抄録
Computer simulation with finite element model (FEM) plays more and more important role in design stage. For the sake that there are always some differences in physical parameters from those of real structure, it is difficult to make an accurate model actually. This paper contributes the approach to detect the inconsistency between the real structure and the FEM efficiently, only a few necessary measurement locations are required with the sensitivities of eigen frequency and response of structure. Changes in structural parameters, induced by damage, affect the eigensolution matrices and may cause the change of eigen frequency and response of structure. Use of these values to select modes and measurement points for use in damage location or sensor placement studies is proposed and demonstrated by application to a simply supported plate. Learning Vector Quantization (LVQ) Neural Network besed on pattern classifier is used to detect the location of damage. Results show that the LVQ has the superiority to Holographic Neural Network in pattern classification.
著者関連情報
© 社団法人日本機械学会
前の記事 次の記事
feedback
Top