Unformatted text preview: iodic Function No No No No Yes No Yes No Yes No No No No Yes Burst Length No No Yes (75%) No No No No Yes (75%) Yes (75%) Yes (75%) No No No No Window Function Hann Hann Uniform Uniform Uniform Uniform Uniform Uniform Uniform Uniform Hann Hann Uniform Uniform Nd
0 0 0 4 4 3 3 0 0 0 0 0 3 3 Nc
1 5 5 1 1 1 1 5 5 8 1 8 2 2 N avg
20 4 4 4 4 5 5 4 4 12 96 12 4 4 Total Blocks 20 20 20 20 20 20 20 20 20 20 96 96 20 20 Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8 Case 9 Case 10 Case 11 Case 12 Case 13 Case 14 Random Random Random Random Random Random Random Random Random Random Random Random Random Random TABLE 55. Test Cases  Excitation/Averaging/DSP Parameters Case 1 (Figure 525) is considered a baseline case since this a very popular method for making a FRF measurement and it can be easily made on all data acquisition equipment. However, it is clear that in this measurement situation, there is a signiﬁcant drop in the multiple coherence function at frequencies consistent with the peaks in the FRF measurement. This characteristic drop in multiple (or ordinary) coherence is often an indication of a leakage problem. This can be conﬁrmed if a leakage reduction method reduces or eliminates the problem when the measurement is repeated. In all subsequent cases, the test conﬁguration was not altered in any way  data was acquired simply using different excitation, averaging and digital signal processing combinations. Case 2 (Figure 526) demonstrates an improvement over Case 1 when the same total measurement time is used but cyclic averaging is used to reduce the leakage error. Case 3 (Figure 527) further demonstrates that burst random with cyclic averaging improves the measurement further. Again the total measurement time remains the same. Cases 4 through 7 (Figures 528 through 531) demonstrate the quality of FRF measurements (561) +UCSDRLRJA CN20263663/664 Revision: June 12, 2001 + that can be achieved with pseudo and periodic random excitation methods with very few power spectral averages. Cases 8 and 9 (Figures (532 through 533) are hybrid techniques involving the combination of burst random with pseudo and periodic random excitation together with cyclic averaging. Case 10 (Figure 534) demonstrates that Case 3 can be marginally improved with more averages, both cyclic and power spectral averages. However, Case 11 (Figure 535) demonstrates that Case 1 (Random with Hann Window) cannot be improved by adding power spectral averages. This is a popular misconception that adding power spectral averages will improve the FRF estimate. This is clearly not true for this case. Case 12 (Figure 36) demonstrates that additional cyclic averages, together with power spectral averages, is an improvement over Case 2 but the improvement is not signiﬁcant considering the additional measurement time. Finally, Cases 13 and 14 (Figures 537 through 538) demonstrate that, when pseudo and periodic random excitation is coupled with cyclic averaging, a...
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 Fall '11
 Regalla
 Signal Processing, GFF Ni Ni, GFF Ni

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