A method for estimating rainflow fatigue damage of narrowband non-Gaussian random loadings

We describe efforts to improve the accuracy of fatigue damage estimation methods of narrowband non-Gaussian random loading. The available analytical solutions are reviewed and briefly summarized, and the reasons for the occurrence of computational errors during nonlinear transformation-based methods are determined. The computational errors are mainly due to inconsistencies in the statistical moments above fourth order. A new approach is proposed for the evaluation of rainflow fatigue damage. This approach avoids the problem of transformation-based methods and provides accurate estimation for fatigue damage of narrowband leptokurtic non-Gaussian random loading. Additionally, the applicability of the proposed method to Gaussian random loading is investigated. Finally, two examples are carried out and comparisons are made to more commonly used methods to demonstrate the capabilities and brevity of the proposed algorithm.

[1]  Variable stress ratio in cumulative fatigue damage: Experiments and comparison of three models , 2010 .

[2]  W. Eichlseder,et al.  Prediction of the low cycle fatigue regime of the S–N curve with application to an aluminium alloy , 2012 .

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

[4]  Nigel Barltrop,et al.  A new look at the effect of bandwidth and non-normality on fatigue damage , 2001 .

[5]  Vincent Rouillard.  The Synthesis of Road Vehicle Vibrations Based on the Statistical Distribution of Segment Lengths , 2007 .

[6]  Rick S. Blum,et al.  On the Approximation of Correlated Non-Gaussian Noise Pdfs using Gaussian Mixture Models , 1999 .

[7]  Jian-Qiao Sun,et al.  Multi-stage regression fatigue analysis of non-Gaussian stress processes , 2005 .

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

[9]  Raouf A. Ibrahim,et al.  Numerical and experimental studies of linear systems subjected to non-Gaussian random excitations , 1999 .

[10]  Shahram Sarkani,et al.  Stochastic fatigue damage accumulation under broadband loadings , 1995 .

[11]  Pär Johannesson,et al.  Rainflow Analysis of Switching Markov Loads , 1999 .

[12]  Brian M. Sadler,et al.  Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures , 2000, IEEE Trans. Signal Process..

[13]  Jwo Pan,et al.  Fatigue Testing and Analysis: Theory and Practice , 2004 .

[14]  Denis Benasciutti,et al.  Fatigue life assessment in non-Gaussian random loadings , 2006 .

[15]  L. Lutes,et al.  Analytical Methods For Non-Gaussian Stochastic Response of Offshore Structures , 1997 .

[16]  Igor Rychlik,et al.  Rain-flow fatigue damage for transformed gaussian loads , 2007 .

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

[18]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[19]  Igor Rychlik,et al.  Distribution of wave crests in a non-Gaussian sea , 2009 .

[20]  Igor Rychlik,et al.  WAFO - A Matlab Toolbox For Analysis of Random Waves And Loads , 2000 .

[21]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[22]  Quanke Feng,et al.  Dynamic simulation and stress analysis for reciprocating compressor crankshaft , 2013 .

[23]  Turan Dirlik,et al.  Application of computers in fatigue analysis , 1985 .