TY - GEN
T1 - Atomic norm minimization for modal analysis with random spatial compression
AU - Li, Shuang
AU - Yang, Dehui
AU - Wakin, Michael B.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Identifying characteristic vibrational modes and frequencies is of great importance for monitoring the health of structures such as buildings and bridges. In this work, we address the problem of estimating the modal parameters of a structure from small amounts of vibrational data collected from wireless sensors distributed on the structure. We consider a randomized spatial compression scheme for minimizing the amount of data that is collected and transmitted by the sensors. Using the recent technique of atomic norm minimization, we show that under certain conditions exact recovery of the mode shapes and frequencies is possible. In addition, in a simulation based on synthetic data, our method outperforms a singular value decomposition (SVD) based method for modal analysis that uses the uncompressed data set.
AB - Identifying characteristic vibrational modes and frequencies is of great importance for monitoring the health of structures such as buildings and bridges. In this work, we address the problem of estimating the modal parameters of a structure from small amounts of vibrational data collected from wireless sensors distributed on the structure. We consider a randomized spatial compression scheme for minimizing the amount of data that is collected and transmitted by the sensors. Using the recent technique of atomic norm minimization, we show that under certain conditions exact recovery of the mode shapes and frequencies is possible. In addition, in a simulation based on synthetic data, our method outperforms a singular value decomposition (SVD) based method for modal analysis that uses the uncompressed data set.
KW - Modal analysis
KW - atomic norm minimization
KW - spectral analysis
KW - structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85023768088&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952757
DO - 10.1109/ICASSP.2017.7952757
M3 - Conference Proceeding
AN - SCOPUS:85023768088
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3251
EP - 3255
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -