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INNOVATIVE APPROACHES TO STRUCTURAL HEALTH MONITORING
The normalized Chebyshev coefficients are generated as a result of fitting the
computed restoring force from the prescribed data set containing the system
displacement and velocity to the measureoVsimulated force response. One advantage
of this method is that upon a relatively straight forward transformation of Equation 2
to a Power Series expansion [3], the coefficients can be correlated to recognizable
physical systems. This property allows a simple verification of the identified results
against a known standard and facilitates understanding of the root cause of identified
system degradation.
23. Sample Results
The identification algorithm presented above is easily adapted to statistical studies
drawn from large ensemble data sets, enabling the establishment of identification
results under system parameter uncertainty or variability.
55155 11231 1684S5 22462 55262 110524 165.786 221.048
(a)
(b)
Figure 2.2 Chebyshev coefficient (2,1), (a) 5% stiffness degradation with 10% noise
pollution, (b) 5% epsilon degradation with 10% noise pollution
The robustness of the identification algorithm in detecting subtle changes to system
parameters was evaluated by implementing a Monte Carlo approach. System
parameters were varied from the prescribed system mean values noted above. 3000
simulations were then performed with a zero-mean random excitation. After
computing the mean and standard deviation statistics of these runs, plots of individual
parameters highlighted excursions from prescribed values, such as in Figure 2.2.