TY - JOUR
T1 - Damage identification in aircraft structures with self-powered sensing technology
T2 - A machine learning approach
AU - Salehi, Hadi
AU - Das, Saptarshi
AU - Chakrabartty, Shantanu
AU - Biswas, Subir
AU - Burgueño, Rigoberto
N1 - Funding Information:
The research described in this paper was carried out with funding from the U.S. National Science Foundation under grant CNS‐1405273.
Funding Information:
National Science Foundation, Grant/ Award Number: CNS‐1405273
Publisher Copyright:
© 2018 John Wiley & Sons, Ltd.
PY - 2018/12
Y1 - 2018/12
N2 - Progress in self-powered wireless sensor networks for structural health monitoring (SHM) have motivated the development of power-efficient data communication protocols. One such approach is the energy-aware pulse switching architecture, which employs ultrasonic pulses to transmit binary data through the material substrate. However, this technology creates time delays on the generated data due to the power budgets demanded for sensing and communication. The nature of data collected from such protocol thus requires the development of new analysis and interpretation methods. This study presents a robust damage identification strategy for aircraft structures, within the context of data-driven SHM, using discrete time-delayed binary data. A novel machine learning framework integrating low-rank matrix completion, pattern recognition, k-nearest neighbor, and a data fusion model was developed for damage identification. Performance and accuracy of the proposed data-driven SHM strategy was investigated and tested for an aircraft horizontal stabilizer wing. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The reliability of the proposed strategy with noise-polluted data was also validated. Further, the effect of variations in harvested energy on the performance of the approach was investigated. Results demonstrate that the developed machine learning framework can effectively detect the presence and location of damage based on time-delayed binary data from a self-powered sensor network.
AB - Progress in self-powered wireless sensor networks for structural health monitoring (SHM) have motivated the development of power-efficient data communication protocols. One such approach is the energy-aware pulse switching architecture, which employs ultrasonic pulses to transmit binary data through the material substrate. However, this technology creates time delays on the generated data due to the power budgets demanded for sensing and communication. The nature of data collected from such protocol thus requires the development of new analysis and interpretation methods. This study presents a robust damage identification strategy for aircraft structures, within the context of data-driven SHM, using discrete time-delayed binary data. A novel machine learning framework integrating low-rank matrix completion, pattern recognition, k-nearest neighbor, and a data fusion model was developed for damage identification. Performance and accuracy of the proposed data-driven SHM strategy was investigated and tested for an aircraft horizontal stabilizer wing. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The reliability of the proposed strategy with noise-polluted data was also validated. Further, the effect of variations in harvested energy on the performance of the approach was investigated. Results demonstrate that the developed machine learning framework can effectively detect the presence and location of damage based on time-delayed binary data from a self-powered sensor network.
KW - damage detection
KW - machine learning
KW - matrix completion
KW - pattern recognition
KW - self-powered sensor
KW - time-delayed binary data
UR - http://www.scopus.com/inward/record.url?scp=85053536366&partnerID=8YFLogxK
U2 - 10.1002/stc.2262
DO - 10.1002/stc.2262
M3 - Article
AN - SCOPUS:85053536366
SN - 1545-2255
VL - 25
JO - Structural Control and Health Monitoring
JF - Structural Control and Health Monitoring
IS - 12
M1 - e2262
ER -