Frequency-domain methods for a vibration-fatigue-life estimation – Application to real data

Abstract The characterization of vibration-fatigue strength is one of the key parts of mechanical design. It is closely related to structural dynamics, which is generally studied in the frequency domain, particularly when working with vibratory loads. A fatigue-life estimation in the frequency domain can therefore prove advantageous with respect to a time-domain estimation, especially when taking into consideration the significant performance gains it offers, regarding numerical computations. Several frequency-domain methods for a vibration-fatigue-life estimation have been developed based on numerically simulated signals. This research focuses on a comparison of different frequency-domain methods with respect to real experiments that are typical in structural dynamics and the automotive industry. The methods researched are: Wirsching–Light, the α0.75 method, Gao–Moan, Dirlik, Zhao–Baker, Tovo–Benasciutti and Petrucci–Zuccarello. The experimental comparison researches the resistance to close-modes, to increased background noise, to the influence of spectral width, and multi-vibration-mode influences. Additionally, typical vibration profiles in the automotive industry are also researched. For the experiment an electro-dynamic shaker with a vibration controller was used. The reference-life estimation is the rainflow-counting method with the Palmgren–Miner summation rule. It was found that the Tovo–Benasciutti method gives the best estimate for the majority of experiments, the only exception being the typical automotive spectra, for which the enhanced Zhao–Baker method is best suited. This research shows that besides the Dirlik approach, the Tovo–Benasciutti and Zhao–Baker methods should be considered as the preferred methods for fatigue analysis in the frequency domain.

[1]  John Goodman,et al.  Mechanics applied to engineering , 1904 .

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

[3]  Claudio Braccesi,et al.  Fatigue behaviour analysis of mechanical components subject to random bimodal stress process: frequency domain approach , 2005 .

[4]  Bernardo Zuccarello,et al.  Fatigue life prediction under wide band random loading , 2004 .

[5]  B. Zuccarello,et al.  On the estimation of the fatigue cycle distribution from spectral density data , 1999 .

[6]  A. Palmgren.  Die Lebensdauer von Kugellargern , 1924 .

[7]  Julius S. Bendat,et al.  Probability Functions for Random Responses: Prediction of Peaks, Fatigue Damage, and Catastrophic Failures , 1964 .

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

[9]  R. Rackwitz,et al.  Comparison of analytical counting methods for Gaussian processes , 1993 .

[10]  R. Tovo.  Cycle distribution and fatigue damage under broad-band random loading , 2002 .

[11]  Adam Niesłony,et al.  Comparison of the Rain Flow Algorithm and the Spectral Method for Fatigue Life Determination Under Uniaxial and Multiaxial Random Loading , 2004 .

[12]  Ewald Macha,et al.  Fatigue life calculation by means of the cycle counting and spectral methods under multiaxial random loading , 2005 .

[13]  Angela Halfpenny,et al.  A Frequency Domain Approach for Fatigue Life Estimation from Finite Element Analysis , 1999 .

[14]  Kihong Shin,et al.  Fundamentals of Signal Processing for Sound and Vibration Engineers , 2008 .

[15]  Miha Boltežar,et al.  Modelling localised nonlinearities using the harmonic nonlinear super model , 2006 .

[16]  R. Brook,et al.  Cumulative Damage in Fatigue: A Step towards Its Understanding , 1969 .

[17]  Ying Min Low,et al.  A method for accurate estimation of the fatigue damage induced by bimodal processes , 2010 .

[18]  John W. Miles,et al.  On Structural Fatigue Under Random Loading , 1954 .

[19]  Marko Nagode,et al.  Frequency based fatigue analysis and temperature effect , 2011 .

[20]  M. Baker,et al.  On the probability density function of rainflow stress range for stationary Gaussian processes , 1992 .

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

[22]  D. Benasciutti,et al.  Spectral methods for lifetime prediction under wide-band stationary random processes , 2005 .

[23]  Costas Papadimitriou,et al.  Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering , 2011 .

[24]  Roberto Tovo,et al.  A stress invariant based spectral method to estimate fatigue life under multiaxial random loading , 2011 .

[25]  N. W. M. Bishop.  The use of frequency domain parameters to predict structural fatigue , 1988 .

[26]  David Cebon,et al.  Predicting fatigue lives for bi-modal stress spectral densities , 2000 .

[27]  Shinsuke Sakai,et al.  On the Distribution of Rainflow Range for Gaussian Random Processes with Bimodal PSD , 1995 .

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

[29]  Shahram Sarkani,et al.  Random Vibrations: Analysis of Structural and Mechanical Systems , 2003 .

[30]  E. Vanmarcke.  Properties of Spectral Moments with Applications to Random Vibration , 1972 .

[31]  Claudio Braccesi,et al.  The frequency domain approach in virtual fatigue estimation of non-linear systems: The problem of non-Gaussian states of stress , 2009 .

[32]  Chan-Jung Kim,et al.  Experimental spectral damage prediction of a linear elastic system using acceleration response , 2011 .

[33]  Denis Benasciutti.  Fatigue analysis of random loadings: A frequency-domain approach , 2012 .

[34]  Igor Rychlik,et al.  Fatigue and stochastic loads , 1996 .

[35]  Nuno M. M. Maia,et al.  Theoretical and Experimental Modal Analysis , 1997 .

[36]  Torgeir Moan,et al.  Frequency-domain fatigue analysis of wide-band stationary Gaussian processes using a trimodal spectral formulation , 2008 .

[37]  Paul H. Wirsching,et al.  Fatigue under Wide Band Random Stresses , 1980 .