Reliability Growth and the Caveats of Averaging: a Centaur Case Study
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Spacecraft reliability modeling is plagued by data scarcity and lack of data applicability. Systems tend to be one-of-a- kind, and observed failures tend to be the result of systemic defects or human errors, instead of component failures. The result is too often a gap between two extreme estimating approaches: probabilistic risk assessments (PRA) that are component-based lead to optimistic estimates by ignoring system-level failure modes; while history-based failure frequencies can lead to pessimistic estimates by neglecting non-homogeneity (between vehicles and vehicle configurations), reliability growth, and improvements in design. The problem of non-homogeneity is often considered solved once a system has a sufficiently long history. But in reality, rarely can tens of launches be considered samples of the same probability distribution. Launch vehicles undergo design changes in their history; more accurate estimates of reliability need to account for the risk introduced by design changes and for two types of reliability growth: growth of a given system via systematic tracking, assessing, and correction of the causes of failure uncovered in flights; and general technological or knowledge growth over subsequent generations of the system.
Keywords: Failure Rate - Product Reliability - RAMS 2011 Proceedings - Reliability Analysis/Prediction/Estimation