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Figure 4a shows an example of computing the FM4 for a TSA acceleration signal taken from a baseline OH-58C. The .rst row is one cycle of data partitioned into 99 indi-vidual tooth mesh periods (TMPs). The second row shows the frequency response and its highly periodic nature. The third row shows the remaining signal after the planet mesh, harmonics, and its .rst-order sidebands are removed. This is shown at the same scale as Row 2 to emphasize the re-moval of regular components. Row 3 shows the same re-maining signal at the noise scale where the rejected regions are noticeable. The fourth row represents the difference sig-nal in the time domain obtained by taking the inverse-FFT. The FM4 calculates how normally distributed are the am-plitudes of the difference signal. This example produced a value of 2.95 and a plot of its distribution relative to normal is given in Figure 6a. Figure 4b is an example of a syn-
thesized signal representing an individual planet gear taken from a baseline case of the OH-58C using planet gear vi-bration separation which is discussed later. It is made up of a collection of individual tooth mesh waveforms. These waveforms have a high degree of correlation. A look at the frequency response reveals that this signal is made up of only a few planet mesh components. The third row reveals what is left when the planet mesh and its sidebands are re-moved. The fourth row shows the same resulting signal at the noise .oor scale. The remaining signal is then converted back to the time domain and its distribution of values deter-mined. For this almost perfect case, a value of 5.11 was obtained, illustrating that when working with synthesized signals, the noise .oor may not be as Gaussian as a real signal, resulting in higher than nominal FM4 values. The distribution is given in Figure 6b. |