Wear time-dependent reliability analysis using Bayesian inference

Wear is one of the most important reasons for failures of mechanical systems and components. This paper presents a simulation approach for reducing the uncertainty in the wear time-dependent reliability analysis for a pin of a lifting mechanical system. The wear time-dependent reliability modeling of the pin is established based on Archard theory, and the material parameters, elastic modulus and Poisson’s ratio are treated as uncertain variables. Then, Bayesian inference is used to incorporate the new information to update the probability density functions of elastic modulus and Poisson’s ratio, and reduce the uncertainty. Wear time-dependent reliability of the pin before and after updating are evaluated using PHI2 method which is based on the up-crossing rate approach and allows to solve time-dependent problems using classical time-invariant reliability tools such as FORM/SORM methods.