Methodologies for Predicting Dormant Missile Reliabilities

ABSTRACT: Reliability of weapon systems through periods of storage dormancy has long been a significant concern to military organizations. The trend in missile systems over previous 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, as many missile components are susceptible to environmental degradation in uncontrolled storage, handling and transportation conditions. To achieve better maintenance and repair planning, supply chain upgrades and system modifications, Air Force requires methods to accurately predict wear-out of dormant missile line replaceable units in the absence of remedial action. Thus, the objective of the proposed work is to develop validated, accurate prediction models, algorithms and software to assess and predict reliabilities of dormant aging cruise missile systems based on limited prior line replaceable unit maintenance and test data. In Phase I TRI/Austin developed preliminary statistical methodologies to estimate LRU reliabilities and prospective algorithms to predict future maintenance requirements. In Phase II these will be further tested and validated for predictive capabilities, and partnering with ReliaSoft to develop software and database to automate predictive processes for Air Force maintenance personnel and supply chain managers. BENEFIT: Accurate predictions of line replaceable unit wear-out allow better maintenance and repair planning, supply chain upgrades and system modifications. Commercial availability of the product to be developed from the proposed work will allow reliability assessments of aging dormant systems based on a deficient availability of maintenance or repair data, 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.