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Structural Health Monitoring Using Smart Sensors - ideals ...

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From Eq. (6.26), one obtains<br />

F <br />

V <br />

=<br />

<br />

(6.27)<br />

Eqs. (6.23) and (6.27) indicate that L = V <br />

, i.e., DLVs can be obtained from the<br />

SVD of the difference matrix F <br />

. In Eq. (6.24), because of noise and computational<br />

errors, the singular values corresponding to V <br />

are not exactly zero. To select DLVs from<br />

SVD results of the matrix , Bernal (2002) proposed an index svn defined as<br />

F <br />

svn i<br />

=<br />

<br />

s i c<br />

-------------------------- i<br />

<br />

max s k c k <br />

k<br />

(6.28)<br />

where s i<br />

is the i-th singular value of the matrix F <br />

; c i<br />

is the constant that is used to<br />

normalize the maximum stress in the structural element, which is induced by the state load<br />

c i<br />

V i<br />

, to have a value of one; and V i<br />

is the right singular vector of F <br />

.<br />

Each of the DLVs is then applied to an undamaged analytical model of the structure,<br />

and the stress in each structural element is calculated. If an element has zero normalized<br />

accumulative stress, this element is a candidate damaged element. The normalized<br />

accumulative stress for the j-th element is defined as<br />

j<br />

=<br />

j<br />

k<br />

----------------------<br />

max <br />

k<br />

(6.29)<br />

where<br />

j<br />

=<br />

<br />

abs<br />

<br />

<br />

n<br />

<br />

i=<br />

<br />

<br />

----------------------- ij <br />

max i <br />

k<br />

k<br />

<br />

(6.30)<br />

In Eq. (6.30), ij<br />

is the stress in the j-th element induced by the i-th DLV; j<br />

is the<br />

cumulative stress in the j-th element. In practice, the normalized accumulative stress<br />

induced by DLVs in the damaged elements may not be exactly zero due to noise and<br />

uncertainties. A reasonable threshold should be chosen to select damaged elements.<br />

6.4 Distributed Computing Strategy for SHM<br />

Gao (2005) proposed the Distributed Computing Strategy (DCS) for SHM employing<br />

the algorithms reviewed in previous sections of this chapter. In this approach, a hierarchy<br />

of local sensor communities is organized, so that the data does not need to be centrally<br />

collected or analyzed. Because these algorithms are applicable to data only from nodes in<br />

a local sensor community, the analysis can be performed within a local sensor community<br />

independent of the other nodes; data from a given community is processed within the<br />

97

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