Structural Health Monitoring Using Smart Sensors - ideals ...

Node 1
x1
E
x 1 t x 1
t
Node 2
x2
Node 3
x3
Node 4
x4
Node 5
x5
Node ns
xns
E x 1 t x 2
t
E x 1 t x3
t
E x 1 t x 4
t
E
x 1 t x 5
t
E
x 1 t x ns
t
Figure 5.2. Distributed correlation function estimation.
application. However, Gao (2005) proposed a DCS for SHM which supports this idea.
Neighboring smart sensors in a single-hop communication range make local sensor
communities and perform SHM in the communities. In such applications, the assumption
of nodes being within single-hop range of a reference node is reasonable.
5.1.3 Implementation
. . .
In this section, the data aggregation strategy introduced previously is validated on a
network of Imote2s. To understand the performance of the algorithms, consider the truss
model shown in Figure 5.3. Vertical acceleration responses under random input are
measured at nodes 9, 11, 13, 15, 17, 19, 21, 23, and 25. The responses are then injected
into a network of nine Imote2s. The Imote2 corresponding to node 9 works as the
reference sensor. This node broadcasts its own data to the other nodes and collects
correlation function estimates. The length, N , of the injected data is 2,048 and the number
of FFT points is 512, resulting in seven averages with a 50 percent overlap. The sampling
rate is 500 Hz.
The acceleration response data and correlation function estimates are stored and
transferred as a 16-bit integer data type, considering that accelerometer can reasonably be
assumed to have a 12-bit resolution. In this way, memory space and bandwidth are
efficiently utilized, while double precision data is used during the spectral density
estimation and the inverse FFT calculations.
Simulation data is then reliably transferred to the destination nodes using a reliable
communication protocol described in the next section. In this way, outputs of this
correlation function estimate are compared with those calculated on a PC without being
mixed with the effect of data loss.
Correlation functions are estimated on the Imote2s and reported back to the reference
node. Figure 5.4 shows the estimated correlation function between nodes 9 and 11. This
estimate matches the corresponding correlation function estimated on a PC assuming the
6 10 14 18 22 26 30 34 38 42 46 50
3 7 11 15 19 23
31 35 39 43 47 51
27
2 5 9 13 17 21 25 29 33 37 41 45 49 53
1 4 8 12 16 20 24 28 32 36 40 44 48 52
Figure 5.3. Numerical model of a 2D truss structure.
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