Fatigue assessment of non-stationary random vibrations by using decomposition in Gaussian portions

In the present paper the authors define an original analytical expression of a corrective coefficient to evaluate fatigue damage induced by a non-Gaussian stress state affected by high Kurtosis (values higher than 5) and by zero Skewness. This approach starts from a previous activity in which the authors solved an analogous problem but for light non-Gaussian stress states (Kurtosis value less than 5). The proposed procedure assumes to know the fatigue damage induced by Gaussian equivalent stress state time domain process. This characteristic allows the proposed procedure to be easily adopted inside the so-called Frequency Domain Fatigue Methods but in parallel with the statistical analysis of the system time domain response (Kurtosis and Skewness evaluation). Interesting considerations about its applicability will be proposed as concerns the non-Gaussianity and non-Stationarity of the inputs when the system is a flexible component excited in its frequency range.

Correction formula approach to evaluate fatigue damage induced by non-Gaussian stress state

Abstract The well-known problem with the fatigue lifetime assessment of non-Gaussian loading signals with the use of spectral method has been presented in the paper. A correction factors that transform the non-Gaussian signal into an equivalent Gaussian signal proposed by Bracessi et al. (2009) has been used for the purpose of lifetime calculations together with Palmgren-Miner Hypothesis. The calculations have been performed for the 10HNAP steel under random non-Gaussian load with four dominating frequencies. The signal has been generated on the test stand SHM250 for random tension-compression tests. The results with zero and non-zero mean stresses have been used to calculate the fatigue life with the frequency domain method based on Dirlik’s model and with a time domain method with the use of the rainflow cycle counting algorithm. The obtained calculation results have been compared with experimental results.

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Peter Wolfsteiner

Papers

Deriving Gaussian Fatigue Test Spectra from Measured non Gaussian Service Spectra