Assessing high reliability via Bayesian approach and accelerated tests

Sometimes the assessment of very high reliability levels is difficult for the following main reasons:(a) gh reliability level of each item makes it impossible to obtain, in a reasonably short time, a sufficient number of failures; gh cost of the high reliability items to submit to life tests makes it unfeasible to collect enough data for ‘classical’ statistical analyses. above context, this paper presents a Bayesian solution to the problem of estimation of the parameters of the Weibull–inverse power law model, on the basis of a limited number (say six) of life tests, carried out at different stress levels, all higher than the normal one. er-stressed (i.e. accelerated) tests allow the use of experimental data obtained in a reasonably short time. The Bayesian approach enables one to reduce the required number of failures adding to the failure information the available a priori engineersʹ knowledge. This engineersʹ involvement conforms to the most advanced management policy that aims at involving everyoneʹs commitment in order to obtain total quality. e Carlo study of the non-asymptotic properties of the proposed estimators and a comparison with the properties of maximum likelihood estimators closes the work.