An example of real-world data was examined in terms of its Probability Density Function (PDF) spectrum and compared to a standard controller produced Gaussian distribution and a kurtosis controlled distribution. Data was collected for the acceleration of an automotive dashboard while driving on a gravel road. These measurements were imported into a random vibration control software program, which calculated an average PSD and the kurtosis of the measured data. From these measurements, the kurtosis was evaluated to be 3.8, indicating this environment was non-Gaussian.
Then, using a vibration controller, two random tests were run intending to match this environment. Both tests were controlled to the same measured PSD spectrum and overall RMS values. In one test, a standard algorithm producing a Gaussian signal was used. In the second, a kurtosis control algorithm was used to match the measured 3.8 value.
As shown in Figure 3, the PDF of the kurtosis-controlled test produced a far better match to the field measurements than the Gaussian-controlled test. Note that the field measurement and kurtosis-controlled test both exhibit broader PDF “tails” than the Gaussian test. These tails
indicate the measured environment was more severe than the Gaussian test intended to represent it.