This paper presents a framework for developing part failure-rate models. It is a partial result of an effort sponsored by the US Air Force for the development of reliability prediction models for military avionics. The other portion of the effort pertains to reliability predictions at the assembly and system levels, and is not covered here. Published data show that the existing reliability prediction methods fall far short of providing the required accuracy. One of the problems in the existing methods is the exclusion of critical factors. The new framework is based on the premise that essentially all failures are caused by the interactions of built-in flaws, failure mechanisms, and stresses. These three ingredients contribute to form the failure distribution which are functions of stress application duration (eg, aging time), number of thermal cycles, and vibration duration. The Weibull distribution has been selected as the general distribution. The distribution is then modified by the critical factors such as flaw quantities, effects of environmental stress screening, calendar-time reliability improvements, and vendor quality differences, to provide the part failure-rate functions. To provide credibility for the framework, only well published data and information have been used. This paper is not an academic exercise. The recognition of the predicted failure rate characteristics has enabled the author to make many reliability engineering decisions correctly, especially in environmental stress screening procedures and reliability demonstration plans. It is imperative that, prior to the complete development of new prediction models based on this framework, the applicable parts of the model framework and available knowledge on identified critical factors be put into use immediately to improve the accuracy of reliability predictions using existing manuals. This can be done by decomposing the parameter values from the existing manuals and redistributing them in terms of the new model parameters; and adding available new parameters for the neglected factors.
