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v3_5 - UC-SDRL-RJA CN-20-263-663/664 Revision 5 FREQUENCY...

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+UC-SDRL-RJA CN-20-263-663/664 Revision: June 12, 2001 + 5. FREQUENCY RESPONSE FUNCTION MEASUREMENTS 5.1 Introduction For current approaches to experimental modal analysis, the frequency response function is the most important measurement to be made. When estimating frequency response functions, a measurement model is needed that will allow the frequency response function to be estimated from measured input and output data in the presence of noise (errors). Some of the errors are: Digital Signal Processing Errors (Leakage, Aliasing) Noise Equipment problem (Power supply noise) Cabling problems (RFI,EMI) Rattles, cable motion Calibration (operator error) Complete system calibration Transducer calibration Since the frequency response function can be expressed in terms of system properties of mass, stiffness, and damping, it is reasonable to conclude that in most realistic structures, the frequency response functions are considered to be constants just like mass, stiffness, and damping. This concept means that when formulating the frequency response function using H 1 , H 2 , or H v algorithms, the estimate of frequency response is intrinsically unique, as long as the system is linear and the noise can be minimized or eliminated. The estimate of frequency response is valid whether the input is stationary, non-stationary, or deterministic. Therefore, several important points to remember before estimating frequency response functions are: The system (with the boundary conditions for that test) determines the frequency response functions for the given input/output locations. It is important to eliminate or at least minimize all errors (aliasing, leakage, noise, calibration, etc.) when collecting data. (5-1)
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+UC-SDRL-RJA CN-20-263-663/664 Revision: June 12, 2001 + If all noise terms are identically zero, the assumption concerning the source/location of the noise does not matter ( H 1 = H 2 = H v = H s = H ). Therefore, concentrate on eliminating the source of the noise. Since modal parameters are computed from estimated frequency response functions, the modal parameters are only as accurate as the estimated frequency response functions. There are at least four different testing configurations that can be compared. These different testing conditions are largely a function of the number of acquisition channels or excitation sources that are available to the test engineer. In general, the best testing situation is the multiple input/multiple output configuration (MIMO) since the data is collected in the shortest possible time with the fewest changes in the test conditions. Single input/single output. (SISO) Only option if 2 channel data acquisition system. Longest testing time. Roving inputs. Roving outputs. Time invariance problems between measurements.
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