Understanding How Kurtosis Is Transferred from Input Acceleration to Stress Response and Its Influence on Fatigue Llife

High cycle fatigue of metals typically occurs through long term exposure to time varying loads which, although modest in amplitude, give rise to microscopic cracks that can ultimately propagate to failure. The fatigue life of a component is primarily dependent on the stress amplitude response at critical failure locations. For most vibration tests, it is common to assume a Gaussian distribution of both the input acceleration and stress response. In real life, however, it is common to experience non-Gaussian acceleration input, and this can cause the response to be non-Gaussian. Examples of non-Gaussian loads include road irregularities such as potholes in the automotive world or turbulent boundary layer pressure fluctuations for the aerospace sector or more generally wind, wave or high amplitude acoustic loads. The paper first reviews some of the methods used to generate non-Gaussian excitation signals with a given power spectral density and kurtosis. The kurtosis of the response is examined once the signal is passed through a linear time invariant system. Finally an algorithm is presented that determines the output kurtosis based upon the input kurtosis, the input power spectral density and the frequency response function of the system. The algorithm is validated using numerical simulations. Direct applications of these results include improved fatigue life estimations and a method to accelerate shaker tests by generating high kurtosis, non-Gaussian drive signals.

[1]  Jaijeet Roychowdhury,et al.  Cyclostationary noise analysis of large RF circuits with multitone excitations , 1998 .

[2]  M. Ochi,et al.  Probability distribution applicable to non-Gaussian random processes , 1994 .

[3]  Athanasios Papoulis,et al.  Narrow-band systems and Gaussianity , 1972, IEEE Trans. Inf. Theory.

[4]  J. Lacoume,et al.  Statistiques d'ordre supérieur pour le traitement du signal , 1997 .

[5]  D. Newland An introduction to random vibrations and spectral analysis , 1975 .

[6]  M. Matsuichi,et al.  Fatigue of metals subjected to varying stress , 1968 .

[7]  H. Saunders,et al.  Probability, Random Variables and Stochastic Processes (2nd Edition) , 1989 .

[8]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[9]  Denis Benasciutti,et al.  Cycle distribution and fatigue damage assessment in broad-band non-Gaussian random processes , 2005 .

[10]  S. Winterstein Nonlinear Vibration Models for Extremes and Fatigue , 1988 .

[11]  David Smallwood Vibration with Non-Gaussian Noise , 2009 .

[12]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[13]  S. Rice Mathematical analysis of random noise , 1944 .

[15]  H. Saunders Literature Review : RANDOM DATA: ANALYSIS AND MEASUREMENT PROCEDURES J. S. Bendat and A.G. Piersol Wiley-Interscience, New York, N. Y. (1971) , 1974 .

[16]  Stephen A. Rizzi,et al.  On the Response of a Nonlinear Structure to High Kurtosis Non-Gaussian Random Loadings , 2011 .

[17]  A. Izenman Introduction to Random Processes, With Applications to Signals and Systems , 1987 .

[18]  Darrell F. Socie,et al.  Simple rainflow counting algorithms , 1982 .

[19]  Christian Lalanne Mechanical vibration & shock , 2002 .

[20]  K. Sweitzer RANDOM VIBRATION RESPONSE STATISTICS FOR FATIGUE ANALYSIS OF NONLINEAR STRUCTURES , 2006 .

[21]  H. Saunders,et al.  Book Reviews : AN INTRODUCTION TO RANDOM VIBRATION AND SPECTRAL ANALYSIS D.E. Newland Longman's Inc., New York, NY, 1978 , 1980 .

[22]  E. W. C. Wilkins,et al.  Cumulative damage in fatigue , 1956 .