The ability to accurately predict the probability of equipment successful operation before its actual use would enhance the air force's decision strategy. Documented investigations showed that the existing reliability prediction models fall short of providing the required prediction accuracy and a better model is needed. The key problems in the existing models lie in their exclusion of critical factors. For example, the real vibrational condition of a unit in a fighter could be very different from the average condition assumed under the general condition called out in mil-hdbk-217. Aircraft usage could greatly vary the thermal cycling rate of avionics, but thermal cycling is not explicitly treated. besides the real environments, selection of part suppliers, system aging, environmental stress screening, and parts reliability improvement in calendar time are also not explicitly treated. The proposed research for developing a credible reliability prediction model is to expand the present part stress method of estimating failure rates to include all of the critical factors as they relate to the actual failure mechanisms and applied stresses. An algorithm will also be developed to merge the failure rates to cover multi-condition missions i.e. Covering storage, shipping, flight, etc. In one mission reliability estimate.
