Reliability analysis of steel plate welded joints considering uncertain data

Abstract Fatigue reliability analysis based upon limited and uncertain data brings uncertainties in the inputs such as probability distributions and their respective parameters. However, in practice, the data is compiled by conducting physical tests. The uncertainties based on limited physical test data need to be carefully evaluated. Underestimation or overestimation of the reliability based upon uncertain data and the variability in experimental conditions needs to be evaluated. The uncertainties pertaining to distribution are mitigated using statistical tools. Literature is available wherein either parametric or non-parametric distributions are used to estimate Reliability for uncertain data. However, this paper attempts to use both parametric as well as non-parametric distributions on a set of uncertain data and tries to compare the Reliability. First, the experimental data is assumed to follow the Weibull and Lognormal distribution. The fit of these distributions with the assumed distributions are evaluated and then the reliability is estimated for these distributions. Since the data is limited and uncertain, a non-parametric estimator such as the Kaplan-Meier estimate is used to compute reliability. This paper attempts to fit appropriate failure distribution to uncertain data. The approach is applied on steel plate welded joints and the data on a number of cycles up to failure was studied.