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Proceedings Paper

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data
Author(s): Hadi Salehi; Saptarshi Das; Shantanu Chakrabartty; Subir Biswas; Rigoberto Burgueño
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Paper Abstract

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer’s skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

Paper Details

Date Published: 12 April 2017
PDF: 13 pages
Proc. SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 101680X (12 April 2017); doi: 10.1117/12.2260118
Show Author Affiliations
Hadi Salehi, Michigan State Univ. (United States)
Saptarshi Das, Michigan State Univ. (United States)
Shantanu Chakrabartty, Washington Univ. in St. Louis (United States)
Subir Biswas, Michigan State Univ. (United States)
Rigoberto Burgueño, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 10168:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017
Jerome P. Lynch, Editor(s)

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