# Match the time domain input and output signals to the

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Unformatted text preview: pose of this paper, the burst length refers to the percentage of the total number of contiguous delay and capture blocks. Power Spectral Averages - The number of power spectral averages (N avg or N a ) is the number of auto and cross spectra that are averaged together to estimate the FRF measurements. The actual amount of test time contributing to each power spectral average is a function of the number of contiguous delay and capture blocks. The purpose of power spectral averages is to eliminate the noise that is random with respect to the averaging procedure in order to reduce the variance on the resulting FRF estimate. This type of averaging does not reduce the effects of bias errors like the leakage error. In order to clarify the preceding terminology, Figure 5-13 is a schematic representation of the number of contiguous blocks of time domain data contributing to one power spectral average. In this example, the two blocks marked "D" represent delay blocks and the four blocks marked "C" represent capture blocks. The total time for each power spectral average is, therefore, six contiguous blocks of time data (6 × T seconds of data). (5-44) +UC-SDRL-RJA CN-20-263-663/664 Revision: June 12, 2001 + 0 D 0 1 D 2 Burst Length (%) C 3 C 4 C 5 C 100 6 Number of Contiguous Time Blocks (6T) Window Function Figure 5-13. Total Contiguous Time Per Power Spectral Average (Ensemble) 5.3.3 Classiﬁcation of Excitation Inputs which can be used to excite a system in order to determine frequency response functions belong to one of two classiﬁcations. The ﬁrst classiﬁcation is that of a random signal. Signals of this form can only be deﬁned by their statistical properties over some time period. Any subset of the total time period is unique and no explicit mathematical relationship can be formulated to describe the signal. Random signals can be further classiﬁed as stationary or non-stationary. Stationary random signals are a special case where the statistical properties of the random signals do not vary with respect to translations with time. Finally, stationary random signals can be classiﬁed as ergodic or non-ergodic. A stationary random signal is ergodic when a time average on any particular subset of the signal is the same for any arbitrary subset of the random signal. All random signals which are commonly used as input signals fall into the category of ergodic, stationary random signals. The second classiﬁcation of inputs which can be used to excite a system in order to determine frequency response functions is that of a deterministic signal. Signals of this form can be represented in an explicit mathematical relationship. Deterministic signals are further divided into periodic and non-periodic classiﬁcations. The most common inputs in the periodic deterministic signal designation are sinusoidal in nature while the most common inputs in the (5-45) +UC-SDRL-RJA CN-20-263-663/664 Revision: June 12, 2001 + non-periodic determi...
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