Bayesian Information Fusion Method for Reliability Assessment of Milling Head

Abstract

Reliability assessment of milling head is deemed as one of the critical issues within the field of reliability engineering for machining centers,which is suffered from the difficulty induced by small sample size and expensive reliability tests.A novel reliability assessment method is introduced by incorporating the cost-effective accelerated degradation test(ADT) with the Bayesian information fusion method.The accelerated degradation test is utilized to derive the reliability model of the milling head,to accumulate prior information,and to generate pseudo-lifetime data.The Bayesian method is implemented to construct the reliability assessment model by incorporating the information obtained in the ADT with available field data.Finally,reliability assessment is carried out based on this Bayesian model.To illustrate the approach,an application to a milling head of a gantry machining center is investigated.A comparison of the estimated results between the proposed method and the ADT data based or field data based Bayesian methods is presented.The proposed method is demonstrated more precise and flexible for practical use than others.