TY - JOUR
T1 - Sparsity-based approaches for damage detection in plates
AU - Sen, Debarshi
AU - Aghazadeh, Amirali
AU - Mousavi, Ali
AU - Nagarajaiah, Satish
AU - Baraniuk, Richard
N1 - Funding Information:
The authors gratefully acknowledge the funding from Texas Instruments (TI), TI-168 G84040/G83198 , for this project. The authors thank Dr. Dabak Anand at TI for the useful comments during the course of this work. The authors also thank Dr. Amardeep Sathyanarayana and Dr. Domingo Garcia from TI and Mr. Albert Daniel Neumann from Rice for their help in setting up of the hardware for the experiments.
Publisher Copyright:
© 2018 Elsevier Ltd
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2019/2/15
Y1 - 2019/2/15
N2 - The data deluge in Structural Health Monitoring (SHM) and the need for automated online damage detection systems necessitates a move away from traditional model-based approaches. To that end, we propose sparsity-based algorithms for damage detection in plates. Instead of high-fidelity models, our proposed algorithms use dictionaries, consisting of response signals acquired directly from the system of interest, as the key feature to both detect and localize damages. We address the damage detection problem both when the damage is located on or off a grid of possible damage coordinates defined by the dictionary. This gives rise to two classes of problems, namely, on the grid and off the grid problems. In our sparsity-based on the grid damage detection (SDD-ON) platform, we solve a LASSO regression problem, where, the unknown vector is a pointer for existence of damage at the various locations defined on the grid used for dictionary construction. In our proposed off the grid damage detection (SDD-OFF) platform, we use a penalized regression algorithm to extend the dictionary of measured damage signals to points off-the-grid by linear interpolation. We evaluate the performance of both SDD frameworks, in detecting damages on plates, using finite element simulations as well as laboratory experiments involving a pitch-catch setup using a single actuator-sensor pair. Our results suggest that the proposed algorithms perform damage detection in plates efficiently. We obtain area under receiver operating characteristic (ROC) curves of 0.997 and 0.8314 for SDD-ON and SDD-OFF, respectively.
AB - The data deluge in Structural Health Monitoring (SHM) and the need for automated online damage detection systems necessitates a move away from traditional model-based approaches. To that end, we propose sparsity-based algorithms for damage detection in plates. Instead of high-fidelity models, our proposed algorithms use dictionaries, consisting of response signals acquired directly from the system of interest, as the key feature to both detect and localize damages. We address the damage detection problem both when the damage is located on or off a grid of possible damage coordinates defined by the dictionary. This gives rise to two classes of problems, namely, on the grid and off the grid problems. In our sparsity-based on the grid damage detection (SDD-ON) platform, we solve a LASSO regression problem, where, the unknown vector is a pointer for existence of damage at the various locations defined on the grid used for dictionary construction. In our proposed off the grid damage detection (SDD-OFF) platform, we use a penalized regression algorithm to extend the dictionary of measured damage signals to points off-the-grid by linear interpolation. We evaluate the performance of both SDD frameworks, in detecting damages on plates, using finite element simulations as well as laboratory experiments involving a pitch-catch setup using a single actuator-sensor pair. Our results suggest that the proposed algorithms perform damage detection in plates efficiently. We obtain area under receiver operating characteristic (ROC) curves of 0.997 and 0.8314 for SDD-ON and SDD-OFF, respectively.
KW - Damage detection
KW - Sparsity
KW - Structural Health Monitoring
KW - Wave propagation
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U2 - 10.1016/j.ymssp.2018.08.019
DO - 10.1016/j.ymssp.2018.08.019
M3 - Article
AN - SCOPUS:85051412776
VL - 117
SP - 333
EP - 346
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
ER -