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An optical soft-sensor based shape sensing using a bio-inspired pattern recognition technique to realise fly-by-feel capability for intelligent aircraft operation

Published online by Cambridge University Press:  15 November 2018

M. Basu Open the ORCID record for M. Basu [Opens in a new window]  and
S. K. Ghorai
M. Basu*
Affiliation:
Department of Electronics and Comm. Engg.Birla Institute of TechnologyMesra, RanchiIndia
S. K. Ghorai*
Affiliation:
Department of Electronics and Communication EngineeringBirla Institute of TechnologyMesra, RanchiJharkhandIndia
*
mainak.basu87@gmail.com
skghorai@bitmesra.ac.in
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Abstract

Information regarding deformations in large and complex systems is necessary in the prediction of structural failures caused by un-natural flexural occurrences. Sensing systems which are used to predict shapes, in order to develop a global surface picture require high precision and lower time lag. In this work, a unique bio-inspired training mechanism for support vector regression is presented for shape sensing in structures mounted with Fiber Bragg Gratings. Experimental validation was carried out on a simply supported beam, loaded at different positions and an aircraft wing model for different types of bending. The resulting deflections at specified locations along the length of the beam and on both surfaces of the wing were interpreted from the wavelength shifts of the corresponding Fiber Bragg Gratings through the specially modified Support Vector Regression. The method has shown high accuracy, low computational requirements and enhanced prediction times. The proposed bio-inspired training method has also been compared with two conventional training methodologies.

Keywords

Fiber Bragg GratingShape sensingSupport vector regressionBio-inspired multi-target single modelDecoding variableoptical soft-sensor
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1257 , November 2018 , pp. 1734 - 1752
Copyright
© Royal Aeronautical Society 2018 

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