TY - GEN
T1 - Development of a remote acquisition and transmission system of strain measurements in an unmanned aerial vehicle for damage detection
AU - Alvarez-Montoya, Joham
AU - Carvajal-Castrillón, Alejandro
AU - Betancur-Agudelo, Leonardo
AU - Amaya-Fernández, Ferney
AU - Nino-Navia, Juliana
AU - Sierra-Pérez, Julián
N1 - Funding Information:
The authors are grateful to Centro de Investigación para el Desarrollo y la Innovacin CIDI from Universidad Pontificia Bolivariana for funding this research under Grant No. 636B06/16-57.
PY - 2017
Y1 - 2017
N2 - Reliability is a key requirement in the aerospace industry. Therefore, structural health monitoring (SHM) applications using strain-field estimation and pattern recognition techniques are in development as an alternative to improve reliability and reduce maintenance costs, promising to ease damage detection on aerospace structures. However, some challenges need to be resolved before real implementation of these techniques. One of these challenges is to uncouple operational conditions changes in strain field from those related directly to damage. In this paper, the development of a strain measurement remote acquisition and transmission system on an unmanned aerial vehicle (UAV) using Fiber Grating Sensors (FBGs) is presented. Before flight testing, ground tests are carried out to emulate the dynamic loads that will be presented during flight. The strain data acquired are processed using unsupervised learning algorithms based on Self-Organizing Maps (SOM) and DS2L-SOM (density-based techniques) in order to classify different operational conditions. The results showed the capability of the system for classifying different load conditions for a UAV's main beam.
AB - Reliability is a key requirement in the aerospace industry. Therefore, structural health monitoring (SHM) applications using strain-field estimation and pattern recognition techniques are in development as an alternative to improve reliability and reduce maintenance costs, promising to ease damage detection on aerospace structures. However, some challenges need to be resolved before real implementation of these techniques. One of these challenges is to uncouple operational conditions changes in strain field from those related directly to damage. In this paper, the development of a strain measurement remote acquisition and transmission system on an unmanned aerial vehicle (UAV) using Fiber Grating Sensors (FBGs) is presented. Before flight testing, ground tests are carried out to emulate the dynamic loads that will be presented during flight. The strain data acquired are processed using unsupervised learning algorithms based on Self-Organizing Maps (SOM) and DS2L-SOM (density-based techniques) in order to classify different operational conditions. The results showed the capability of the system for classifying different load conditions for a UAV's main beam.
UR - http://www.scopus.com/inward/record.url?scp=85032456199&partnerID=8YFLogxK
U2 - 10.12783/shm2017/13852
DO - 10.12783/shm2017/13852
M3 - Ponencia publicada en las memorias del evento con ISBN
AN - SCOPUS:85032456199
T3 - Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
SP - 77
EP - 85
BT - Structural Health Monitoring 2017
A2 - Chang, Fu-Kuo
A2 - Kopsaftopoulos, Fotis
PB - DEStech Publications
T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Y2 - 12 September 2017 through 14 September 2017
ER -