Structural Health Monitoring Using Smart Sensors - ideals ...
Structural Health Monitoring Using Smart Sensors - ideals ...
Structural Health Monitoring Using Smart Sensors - ideals ...
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Normalized accumulated stress<br />
measurement 3 measurement 2 measurement 1<br />
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Normalized accumulated stress<br />
measurement 3 measurement 2 measurement 1<br />
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Element ID<br />
(a)<br />
0<br />
1112 1314 15 1617 18 19 15 16 17 18 19 20 21 22 23 19 20 21 22 23 24 25 26 27<br />
Element ID<br />
(b)<br />
Figure 8.20. Damage detection (damaged element: Element 21): (a) mass perturbation<br />
DLV; and (b) SDLV.<br />
1<br />
1<br />
Normalized accumulated stress<br />
measurement 3 measurement 2 measurement 1<br />
0.5<br />
0.3<br />
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1<br />
0.5<br />
0.3<br />
0<br />
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Normalized accumulated stress<br />
measurement 3 measurement 2 measurement 1<br />
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Element ID<br />
(a)<br />
Figure 8.21. Damage detection (damaged element: Element 22): (a) mass perturbation<br />
DLV; and (b) SDLV.<br />
0<br />
11 12 13 14 15 16 17 18 19 15 16 17 18 19 20 21 22 23 19 20 21 22 23 24 25 26 27<br />
Element ID<br />
(b)<br />
false-positives. The cells corresponding to false-negatives are shaded to distinguish falsenegatives<br />
from false-positives. The number of false damage detection using the mass<br />
perturbation DLV method is, overall, larger than the number for the SDLV method,<br />
though the sample size is small.<br />
Tables 8.4 to 8.7 also suggest a way for robust damage detection. The summary of<br />
damage detection using the SDLV method in Tables 8.6 and 8.7 shows that the percentage<br />
of false damage detection cases is always smaller than or equal to 33 percent, while the<br />
percentage for the mass perturbation DLV method can be larger than 50 percent. From this<br />
observation, reliable damage localization might be possible by repeating damage detection<br />
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