Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm

Author

D´Angelo, Giuseppe ; Rampone, Salvatore

Author_Institution

Dept. of Sci. & Technol., Univ. of Sannio, Benevento, Italy

fYear

2014

fDate

29-30 May 2014

Firstpage

408

Lastpage

412

Abstract

This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.

Keywords

Linux; aerospace computing; aerospace industry; condition monitoring; learning (artificial intelligence); materials testing; mechanical engineering computing; multiprocessing systems; parallel processing; pattern classification; HPC implemented soft computing algorithm; HPC parallel implementation; Linux-based cluster; U-BRAIN; aerospace structure defects diagnosis; aerospace structures; composite materials testing; data processing complexity; defect classifier; learning algorithm; multicore architecture; multiparameter data processing; Aerospace materials; Complexity theory; Composite materials; Data processing; Eddy currents; Testing; HPC; Non-destructive testing; eddy current; learning algorithm; parallel computing; signature-based classifier;

fLanguage

English

Publisher

ieee

Conference_Titel

Metrology for Aerospace (MetroAeroSpace), 2014 IEEE

Conference_Location

Benevento

Type

conf

DOI

10.1109/MetroAeroSpace.2014.6865959

Filename

6865959