TY - GEN
T1 - Localized damage identification of plate-like structures with time-delayed binary data from a self-powered sensor network
AU - Salehi, Hadi
AU - Burgueño, Rigoberto
AU - Das, Saptarshi
AU - Biswas, Subir
AU - Chakrabartty, Shantanu
N1 - Funding Information:
The research described in this paper was carried out with funding from the U.S. National Science Foundation under grant number CNS-1405273.
Publisher Copyright:
© 2017 ASME.
PY - 2017
Y1 - 2017
N2 - Recent advances in energy harvesting technologies have led to the development of self-powered monitoring techniques that are energy-efficient. This study presents an intelligent damage identification strategy for plate-like structures based on the data provided by a network of self-powered sensors that communicate through a pulse switching protocol, which has been demonstrated as an effective means for minimizing communication energy demands. The energy-aware pulse switching communication architecture uses single pulses instead of multi-bit packets for information delivery, resulting in discrete binary data. A system employing such an energyefficient technology requires dealing with power budgets for sensing and communication of binary data, which leads to time delay constraints. In this paper, a novel machine learning framework incorporating low-rank matrix decomposition, pattern recognition, and a statistical approach is proposed to overcome challenges inherent in algorithm design for damage identification using time-delayed binary data. Performance and effectiveness of the proposed energy-aware damage identification strategy was examined for the case of a dynamically loaded plate. Damage states were simulated on a finite element model by reducing stiffness in a region of the plate. Results show that the presence and location of the damage can be effectively identified even with noisy features and missing data. The performance and applicability of the proposed localized damage detection strategy for plate-like structures using discrete time-delayed binary data from a novel wireless sensor network is thus demonstrated.
AB - Recent advances in energy harvesting technologies have led to the development of self-powered monitoring techniques that are energy-efficient. This study presents an intelligent damage identification strategy for plate-like structures based on the data provided by a network of self-powered sensors that communicate through a pulse switching protocol, which has been demonstrated as an effective means for minimizing communication energy demands. The energy-aware pulse switching communication architecture uses single pulses instead of multi-bit packets for information delivery, resulting in discrete binary data. A system employing such an energyefficient technology requires dealing with power budgets for sensing and communication of binary data, which leads to time delay constraints. In this paper, a novel machine learning framework incorporating low-rank matrix decomposition, pattern recognition, and a statistical approach is proposed to overcome challenges inherent in algorithm design for damage identification using time-delayed binary data. Performance and effectiveness of the proposed energy-aware damage identification strategy was examined for the case of a dynamically loaded plate. Damage states were simulated on a finite element model by reducing stiffness in a region of the plate. Results show that the presence and location of the damage can be effectively identified even with noisy features and missing data. The performance and applicability of the proposed localized damage detection strategy for plate-like structures using discrete time-delayed binary data from a novel wireless sensor network is thus demonstrated.
UR - http://www.scopus.com/inward/record.url?scp=85035797381&partnerID=8YFLogxK
U2 - 10.1115/SMASIS2017-3941
DO - 10.1115/SMASIS2017-3941
M3 - Conference contribution
AN - SCOPUS:85035797381
T3 - ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2017
BT - Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation; Structural Health Monitoring
PB - American Society of Mechanical Engineers
T2 - ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2017
Y2 - 18 September 2017 through 20 September 2017
ER -