Bayesian Estimation of Degradation Model Defined by a Wiener Process

Abstract

The constantly increasing market request of high quality vehicles ask the automotive manufacturers to perform lifetime testing in order to verify the reliability levels of new products. In this paper, we deal with two difficulties in reliability assessment for mechanical parts. On one hand, there is the small number of parts available for testing. On the other hand, there is the problem of wear. In the automotive applications, mechanical components subjected to relative motion of parts have to be designed against wear. In this paper, the Bayesian estimation of Wiener process parameters (usually used to define the degradation process) is studied to improve the estimation accuracy in incorporating the available knowledge on the product. In particular, the finite element results and expert knowledge are considered as ``a priori{''}. For wear prediction by FEM, a model based on Archard law was developed for the brake disc wear.