TY - JOUR
T1 - Bayesian approach for multi-sensor data fusion based on compressed sensing for wireless structural damage signal
AU - Ji, Sai
AU - Chen, Zhen Yu
AU - Guo, Ping
AU - Sun, Ya Jie
AU - Shen, Jian
AU - Wang, Jin
AU - Lai, Chin Feng
N1 - Funding Information:
This work was a project supported by the National Natural Science Foundation of China (61672290, 51305211, 61300237, 61402234, 61402235, 61311140264), six talent peaks project in Jiangsu Province (XYDXXJS-040) and the PAPD.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - To meet the requirements of data compression and data fusion in structural health monitoring (SHM) in wireless sensor networks (WSNs), this paper proposes a novel method of multi-sensor data fusion based on compressed sensing (CS) for wireless structural damage signal, which can realize data fusion and reconstruct sparse signals. First, the damage signals of aviation aluminum plate are measured onto the linear measurement data through inner products with random Gaussian matrix. Next, data fusion of measurement data is realized by the Bayesian algorithm. Finally, the damage signals can be reconstructed by the CS method. The experiment results show that, compared with the existing methods, the proposed approach can save the network bandwidth and energy according to a good data fusion performance, anti-noise property and a better data compression effect. The proposed approach can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm.
AB - To meet the requirements of data compression and data fusion in structural health monitoring (SHM) in wireless sensor networks (WSNs), this paper proposes a novel method of multi-sensor data fusion based on compressed sensing (CS) for wireless structural damage signal, which can realize data fusion and reconstruct sparse signals. First, the damage signals of aviation aluminum plate are measured onto the linear measurement data through inner products with random Gaussian matrix. Next, data fusion of measurement data is realized by the Bayesian algorithm. Finally, the damage signals can be reconstructed by the CS method. The experiment results show that, compared with the existing methods, the proposed approach can save the network bandwidth and energy according to a good data fusion performance, anti-noise property and a better data compression effect. The proposed approach can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm.
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U2 - 10.6138/JIT.2016.17.7.20160415a
DO - 10.6138/JIT.2016.17.7.20160415a
M3 - Article
AN - SCOPUS:85010644436
VL - 17
SP - 1363
EP - 1371
JO - Journal of Internet Technology
JF - Journal of Internet Technology
SN - 1607-9264
IS - 7
ER -