Damaging fatigue cycles determination for random service loadings using mixed Weibull analysis

Abstract

This paper investigates the fatigue damage behaviour of random service loading histories and suggests a criterion to determine fatigue damaging cycles by using probability values. The rainflow strain range distribution is often used in fatigue damage prediction. The existence of very small amplitude of cycles that do not significantly contribute to damage makes the prediction process unnecessarily complex. Therefore, a criterion to determine the size of damaging cycles that contribute significantly to total damage values is desired. In this study, two datasets were compared, i.e. the standard SAE road profile for the suspension system and an experimental data recorded using a strain gauge installed on a vehicle component. Fatigue damage was determined using the Coffin–Manson, Morrow and Smith–Watson–Topper models. The distribution of strain ranges was identified by distribution fitting process. The relation between strain range magnitude and RMS values was explored using the probability of damage occurrence values. The results show that both sets of strain histories data fit well to a mixed Weibull distribution with n subpopulations. The probability of damage occurrence values obtained were very high, i.e. 0.9362 and 0.9987, indicating very high chances of damage incurred by these damaging cycles. Therefore, 2 RMS values is suggested as a criterion for damaging cycles determination.