non-Gaussian Random Vibration Research Articles

Fatigue life prediction model for accelerated testing of electronic components under non-Gaussian random vibration excitations

In this paper, a novel fatigue life prediction model for electronic components under non-Gaussian random vibration excitations is proposed based on random vibration and fatigue theory. This mathematical model comprehensively associates the vibration fatigue life of electronic components, the characteristics of vibration excitations (such as the root mean square, power spectral density, spectral bandwidth and kurtosis value) and the dynamic transfer characteristics of an electronic assembly (such as the natural frequency and damping ratio) together. Meanwhile a detailed solving method was also presented for determining the unknown parameters in the model. To verify the model, a series of random vibration fatigue accelerated tests were conducted. The results obtained show that the predicted fatigue life based on the model agreed with actual testing. This fatigue life prediction model can be used for the quantitative design of vibration fatigue accelerated testing, which can be applied to assess the long-term fatigue reliability of electronic components under Gaussian and non-Gaussian random vibration environments.

On the Shaker Simulation of Wind-Induced Non-Gaussian Random Vibration

Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data is usually non-Gaussian. Two methodologies are presented in this paper for shaker simulation of wind-induced non-Gaussian vibration. The first methodology synthesizes the non-Gaussian signal offline and replicates it on the shaker in the Time Waveform Replication (TWR) mode. A new synthesis method is used to model the non-Gaussian signal as a Gaussian signal multiplied by an amplitude modulation function (AMF). A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD), probability density function (PDF), and loading cycle distribution (LCD) as the field data. The second methodology derives a damage equivalent Gaussian signal from the non-Gaussian signal based on the fatigue damage spectrum (FDS) and the extreme response spectrum (ERS) and reproduces it on the shaker in the closed-loop frequency domain control mode. The PSD level and the duration time of the derived Gaussian signal can be manipulated for accelerated testing purpose. A case study is presented to show that the derived PSD matches the damage potential of the non-Gaussian environment for both fatigue and peak response.

Vibration Testing by Non-Gaussian Random Excitations with Specified Kurtosis. Part II: Numerical and Experimental Results

This paper is a follow-up to a preceding paper (Part I) in which two methods of non-Gaussian random vibration testing with adjustable kurtosis were introduced and motivation for kurtosis control as a way of increasing or decreasing the excitation crest factor was discussed. The current paper (Part II) adds numerical examples of automobile vibration simulation and experimental results for a kurtosis upgrade implemented in the same form of closed-loop control as in industrial shaker controllers. It was observed in experiments that the dynamic range of a kurtosis controller based on the polynomial transformation method was reduced and the handling of resonances worsened notably. These problems also arise with the sigma clipping technique of crest factor limiting. However, there are no such difficulties with the non-Gaussian method of phase manipulation in the inverse fast Fourier transform (IFFT). When using this method, the signal-to-noise ratio, the controller’s dynamic range, and the stabilization time are as good as in standard Gaussian random testing. Evaluation of the performance of the proposed phase selection algorithm has shown that for increased kurtosis it ensures realistic variability of high peaks in terms of their amplitudes and positions, as well as the number of severe peaks per data block. Because of the analytical solution advantage, both methods, the polynomial transformation and the phase selection, meet time restrictions critical for the operation of shaker testing controllers.

Vibration Testing by Non-Gaussian Random Excitations with Specified Kurtosis. Part I: Discussion and Methods

Non-Gaussian random vibration testing with kurtosis control is considered in the paper as a way of increasing or decreasing the excitation crest factor. An increase of crest factor is required for more accurate simulation of ground vehicle vibrations and the opposite action of crest factor decrease is useful in other applications, such as modal testing. Implementing kurtosis as an additional test specification leads to closed-form solutions for the requirement of the excitation high peak behavior being controlled simultaneously with the traditional power spectral density (PSD) control. A method of subjecting Gaussian signals to polynomial transformation is simpler but has an inherent tendency of introducing frequency distortions jeopardizing the PSD simulation. There is no such difficulty with another approach of phase manipulation in the inverse fast Fourier transform since the power spectrum is not influenced by the phases. A universal phase selection procedure capable of modeling non-Gaussian random excitations with a high or low kurtosis has been developed. Because of the analytical solution advantage, the proposed phase method can be implemented in automatic shaker testing systems with closed-loop control. This paper is the first in a series of two publications. Part II will present numerical and experimental results.

Generation of Non-Gaussian Random Vibration Excitation Signal for Reliability Enhancement Test

AbstractDuring environment testing, the time histories of some dynamic environments follow non-Gaussian distribution. It is always assumed that the random vibration simulated follows Gaussian distribution, because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian. To simulate the real environment of product, a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density (PSD), skewness and kurtosis by shot noise. In this way, non-Gaussian random vibration can be produced on traditional electrodynamic shaker. It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker. At last, the wavelet is used to analyze the non-Gaussian signal.