Statistical Modeling for Reliability Assessment Using Rubber Stiffness Data of the Automotive Engine Mount

Abstract

Input variables used in mechanical system analysis or Prognostics and Health Management (PHM) are various, such as life of material, size and property of product. It has uncertainties (Aleatory uncertainty) due to various noises like temperature, mechanical error and various environmental differences during experiments in real field. For example, obtained data by conducting repeatedly the experiment in the identical environment condition have to be same theoretically. However, experimental data have the variation due to the generated error or noise by the various factors and uncertainties. In this study, the improved algorithm how to determine proper distribution of input variable containing uncertainties is proposed using the probabilistic and statistical method. Also, the validity of this improved algorithm is verified using the automotive engine mounts rubber stiffness data.