Index N Frequency (Hz) NMVR1 NMVR2 NSVR1 NSVR2 8 16 362.564 0.0046 0.0730 0.0046 0.0730 19 16 363.860 0.0034 0.0543 0.0034 0.0543 10 16 557.055 0.0001 0.0014 0.0001 0.0014 12 16 761.224 0.0007 0.0092 0.0007 0.0093 2 17 764.190 0.0020 0.0312 0.0020 0.0312 16 16 1222.980 0.0017 0.0213 0.0017 0.0213 13 16 1224.055 0.0013 0.0185 0.0013 0.0184 4 17 1328.036 0.0042 0.0718 0.0042 0.0717 17 16 1328.803 0.0022 0.0342 0.0022 0.0342 6 17 2019.269 0.0054 0.0763 0.0054 0.0763 23 15 2023.802 0.0047 0.0537 0.0047 0.0537 26 10 2321.862 0.0162 0.1617 0.0162 0.1617 28 8 2322.335 0.0161 0.1279 0.0161 0.1278 22 15 2337.895 0.0020 0.0238 0.0020 0.0238 TABLE 2. Modal Vector Statistics Index N Frequency (Hz) ModalA (State Vector) ModalA (Mean from cluster) real imaginary magnitude phase real imaginary magnitude phase Std. Dev. 8 16 362.564 3.9663e+002-5.7201e+003 5.7338e+003 -86.03 3.9574e+002-5.7195e+003 5.7332e+003 -86.04 1.3866e+002 19 16 363.860 -4.8353e+001 -4.8716e+003 4.8719e+003 -90.57 4.5311e+001 -4.9271e+003 4.9273e+003 -89.47 3.1271e+002 10 16 557.055 7.3166e+001-9.7205e+003 9.7207e+003 -89.57 8.4818e+001-9.7107e+003 9.7111e+003 -89.50 1.2295e+002 12 16 761.224 9.6911e+001-1.8433e+004 1.8434e+004 -89.70 9.5750e+001-1.8433e+004 1.8433e+004 -89.70 2.1414e+002 2 17 764.190 -3.1250e+002 -7.0511e+003 7.0580e+003 -92.54 -3.0277e+002 -7.0524e+003 7.0589e+003 -92.46 3.1198e+002 16 16 1222.980 1.1082e+003-1.7259e+004 1.7295e+004 -86.33 1.1154e+003-1.7253e+004 1.7289e+004 -86.30 5.3096e+002 13 16 1224.055 -4.6394e+003 -3.0840e+004 3.1187e+004 -98.55 -4.6441e+003 -3.0074e+004 3.0431e+004 -98.78 1.6271e+003 4 17 1328.036 -4.8959e+002 -1.5810e+004 1.5818e+004 -91.77 -4.7949e+002 -1.5803e+004 1.5810e+004 -91.74 8.5516e+002 17 16 1328.803 1.5461e+003-1.4661e+004 1.4742e+004 -83.98 1.5462e+003-1.4659e+004 1.4741e+004 -83.98 2.9335e+002 6 17 2019.269 8.1564e+002-1.3301e+004 1.3326e+004 -86.49 7.3874e+002-1.3345e+004 1.3365e+004 -86.83 6.6888e+002 23 15 2023.802 4.2669e+003-6.3248e+004 6.3392e+004 -86.14 4.1880e+003-6.3244e+004 6.3382e+004 -86.21 4.7741e+003 26 10 2321.862 1.6011e+003-2.9188e+004 2.9232e+004 -86.86 8.1739e+002-3.0454e+004 3.0465e+004 -88.46 5.2745e+003 28 8 2322.335 2.8552e+003-3.6135e+004 3.6247e+004 -85.48 1.7616e+003-3.7289e+004 3.7331e+004 -87.30 6.3617e+003 22 15 2337.895 -1.3348e+003 -5.0815e+004 5.0833e+004 -91.50 -1.3637e+003 -5.0848e+004 5.0867e+004 -91.54 2.6546e+003 TABLE 3. Modal Scaling Statistics The philosophy involved to this point is to allow all possible poles and vectors that demonstrate some degree of physical and statistical significance, in terms of the pole weighted vector consistency, to be retained by the procedure. The rationale is that it will be easier for a user to interact with this set of final estimates and intelligently remove poor estimates based upon the statistical or other information rather than to have to add estimates to the final identified set of modal parameters. Since each mode cluster provides a set of answers, the statistics that can be computed is one easy way to identify poorly estimated modal parameters. These poor estimates will occur based upon excessive noise in/on the measured data, violation of the assumption of the modal parameter estimation algorithms, or inadequate spatial data in terms of input-output sensor locations. Ultimately, the limitations of information theory will dictate whether a satisfactory set of modal parameters will be obtained. 4.10 Step 10: Assess Quality of Results (User Interaction) Once the final set of modal parameters, along with their associated statistics, is obtained, quality can be assessed by many methods that are currently available. The most common example is to perform comparisons between the original measurements and measurements synthesized from the modal parameters. Another common example is to look at physical characteristics of the identified parameters such as reasonableness of frequency and damping values, normal mode characteristics in the modal vectors, and appropriate magnitude and phasing in the modal scaling. Other evaluations that may be helpful can be mean phase correlation (MPC) on the vectors, an Auto-MAC looking for agreement between the modal vectors from conjugate poles or any other method available. 376
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