Methodologies for Predicting Dormant Missile Reliabilities

ABSTRACT: Reliability of weapon systems after periods of storage dormancy has long been a significant concern throughout military history. The trend in missile systems over prior decades has been toward greater sophistication, complexity, and longer periods of dormancy while in transportation and storage. Consideration of these makes assessment and prediction of missile reliability increasingly relevant to tactical outcomes. Many missile components are susceptible to environmental degradation and uncontrolled storage, handling and transportation conditions. Air Force requires methods of accurately predicting dormant missile system line replaceable unit wear-out in the absence of remedial action. Wear out predictions allow better maintenance and repair planning, supply chain upgrades and system modifications. Thus, the objective of the proposed work is to develop validated, accurate prediction models and algorithms to assess and predict reliabilities of dormant aging cruise missile systems based on limited prior line replaceable unit maintenance and test data. The statistical methodology of TRI/Austin"s approach will assess reliability by viewing line replaceable units as renewable or non-renewable systems and estimating their future reliability based on repair and maintenance records correlated to storage environmental conditions. A renewable system is relevant when times between repairs are random, and failure and repair rates are non-trending with time. BENEFIT: Accurate predictions of line replaceable unit wear out allow better maintenance and repair planning, supply chain upgrades and system modifications. The commercial benefits of the product to be developed in the proposed work is that it will allow reliability assessments of aging dormant systems based on a deficient availability of maintenance or repair data, which is a common issue in industry. The continued reliance on older systems past their design life, or in permanent or semi-permanent storage, such as mothballed commercial aircraft, manufacturing machinery, and electronics, could benefit from algorithms such as this, allowing informed decisions regarding investments in continued repair and storage costs to be based on quantitative estimates of future system reliability.