Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-25T12:12:46.527Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Kang, L.-H., Kim, D.-K. and Han, J.-H. Estimation of dynamic structural displacements using fiber Bragg grating strain sensors, Journal of Sound and Vibration, August 2007, 305, 534–542, doi: 10.1016/j.jsv.2007.04.037.Google Scholar
2. Tang, H.-Y., Winkelmann, C., Lestari, W. and La Saponara, V. Composite structural health monitoring through use of embedded PZT sensors, Journal of Intelligent Material Systems and Structures, May 2011, 22, 739–755, doi: 10.1177/1045389X11406303. Google Scholar
3. Mariani, S., Corigliano, A., Caimmi, F., Bruggi, M., Bendiscioli, P. and De Fazio, M. MEMS-based surface mounted health monitoring system for composite laminates, Microelectronics Journal, July 2013, 44, 598–605, doi: 10.1016/j.mejo.2013.03.003.Google Scholar
4. Luyckx, G., Voet, E., Lammens, N. and Degrieck, J. Strain measurements of composite laminates with embedded fibre bragg gratings: criticism and opportunities for research, Sensors (Basel), January 2011, 11, 384–408, doi: 10.3390/s110100384.Google Scholar
5. Fan, Y. and Kahrizi, M. Characterization of a FBG strain gage array embedded in composite structure, Sensors and Actuators A: Physical, June 2005, 121, 297–305, doi: 10.1016/j.sna.2005.01.021. Google Scholar
6. Mahakud, R., Kumar, J., Prakash, O. and Dixit, S.K. Study of the nonuniform behavior of temperature sensitivity in bare and embedded fiber Bragg gratings: experimental results and analysis, Appl Opt, November 2013, 52, 7570–7579, doi: 10.1364/AO.52.007570. Google Scholar
7. Yeo, T.L., Sun, T., Grattan, K.T., Parry, D., Lade, R. and Powell, B.D. Polymer-coated fiber Bragg grating for relative humidity sensing, Sensors J, IEEE , September 2005, 5, 1082–1089, doi: 10.1109/JSEN.2005.847935. Google Scholar
8. Duncan, R.G., Froggatt, M.E., Kreger, S.T., Seeley, R.J., Gifford, D.K., Sang, A.K. and Wolfe, M.S. High-accuracy fiber-optic shape sensing, Proc. of 14 th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, April 2007, pp. 6530, doi: 10.1117/12.720914. Google Scholar
9. Yin, W., Fu, T., Liu, J. and Leng, J. Structural shape sensing for variable camber wing using FBG sensors, Proc. SPIE 7292, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, March 2009, pp. 7292, doi: 10.1117/12.812484. Google Scholar
10. Kim, H.-I., Kang, L.-H. and Han, J.-H. Shape estimation with distributed fiber Bragg grating sensors for rotating structures, Smart Materials and Structures, February 2011, 20, 035011, doi: 10.1088/0964-1726/20/3/035011. Google Scholar
11. Xinhua, Y., Mingjun, W. and Xiaomin, C. Deformation sensing of colonoscope on FBG sensor net, TELKOMNIKA Indonesian Journal of Electrical Engineering, 2012, 10, 2253–2260, doi: 10.11591/telkomnika.v10i8.1693.Google Scholar
12. Bhamber, R.S., Allsop, T., Lloyd, G., Webb, D. and Ania-Castanon, J.D. Real-time 3D shape sensing and reconstruction scheme based upon fibre optic Bragg gratings, Proc. of Advanced Photonics Congress, 2012, p. BTu2E. 7, doi: 10.1364/BGPP.2012.BTu2E.7.Google Scholar
13. Davis, M., Kersey, A., Sirkis, J. and Friebele, E. Shape and vibration mode sensing using a fiber optic Bragg grating array, Smart Materials and Structures, July 1996, 5, 759, doi: 10.1088/0964-1726/5/6/005. Google Scholar
14. Zhang, H., Zhu, X., Gao, Z., Geng, L. and Jiang, F. 1798. Non-visual vibration shape reconstruction for smart plate structure with bonded FBG sensors, Journal of Vibroengineering, November 2015, 17, 3803–3821.Google Scholar
15. Patrick, H., Chang, C. and Vohra, S. Long period fibre gratings for structural bend sensing, Electronics Letters, September 1998, 34, 1773–1775, doi: 10.1049/el:19981237. Google Scholar
16. Sanz, J.A., Galar, M., Jurio, A., Brugos, A., Pagola, M. and Bustince, H. Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system, Applied Soft Computing, July 2014, 20, 103–111, doi: 10.1016/j.asoc.2013.11.009.Google Scholar
17. Liu, Z., Shao, J., Xu, W., Zhang, Y. and Chen, H. Prediction of elastic compressibility of rock material with soft computing techniques, Applied Soft Computing, September 2014, 22, 118–125, doi: 10.1016/j.asoc.2014.05.009. Google Scholar
18. Han, H.-G., Li, Y., Guo, Y.-N. and Qiao, J.-F. A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network, Applied Soft Computing, January 2016, 38, 477–486, doi: 10.1016/j.asoc.2015.09.051. Google Scholar
19. Selakov, A., Cvijetinović, D., Milović, L., Mellon, S. and Bekut, D. Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank, Applied Soft Computing, March 2014, 16, 80–88, doi: 10.1016/j.asoc.2013.12.001. Google Scholar
20. Erdogan, T. Fiber grating spectra, Lightwave Technology J, August 1997, 15, 1277–1294, doi: 10.1109/50.618322. Google Scholar
21. Chang, Y.-W., Hsieh, C.-J., Chang, K.-W., Ringgaard, M. and Lin, C.-J. Training and testing low-degree polynomial data mappings via linear SVM, The Journal of Machine Learning Research, April 2010, 11, 1471–1490.Google Scholar
22. Jahn, K., Deutschländer, A., Stephan, T., Strupp, M., Wiesmann, M. and Brandt, T. Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging, Neuroimage, August 2004, 22, 1722–1731, doi: 10.1016/j.neuroimage.2004.05.017. Google Scholar
23. Marx, E., Deutschländer, A., Stephan, T., Dieterich, M., Wiesmann, M. and Brandt, T. Eyes open and eyes closed as rest conditions: impact on brain activation patterns, Neuroimage, April 2004, 21, 1818–1824, doi: 10.1016/j.neuroimage.2003.12.026. Google Scholar
24. Chang, C.-C. and Lin, C.-J. LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology, April 2011, 2, 27, doi: 10.1145/1961189.1961199. Google Scholar