Measurement on an h frame test structure in a test

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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 5-5. Test Cases - Excitation/Averaging/DSP Parameters Case 1 (Figure 5-25) 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 5-26) 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 5-27) 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 5-28 through 5-31) demonstrate the quality of FRF measurements (5-61) +UC-SDRL-RJA CN-20-263-663/664 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 (5-32 through 5-33) are hybrid techniques involving the combination of burst random with pseudo and periodic random excitation together with cyclic averaging. Case 10 (Figure 5-34) demonstrates that Case 3 can be marginally improved with more averages, both cyclic and power spectral averages. However, Case 11 (Figure 5-35) 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 5-37 through 5-38) demonstrate that, when pseudo and periodic random excitation is coupled with cyclic averaging, a...
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