DISCRETE RELIABILITY-GROWTH MODELS BASED ON A LEARNING-CURVE PROPERTY

A learning-curve property, originally prescribed for describing reliability growth when time to failure is observed, is applied to discrete reliability-growth processes for systems for which only discrete success or failure events are observed (eg, a missile or torpedo). Derivations leading to a well-known discrete reliability-growth model are presented, and the role of implicit assumptions relating to testing strategy is identified. An alternative testing strategy, particularly appropriate for destructive test of very expensive systems, is proposed and a new discrete reliability-growth model is derived. Extensions to both types of discrete reliability-growth models, which account for the distinction between assignable cause (readily correctable failure modes) and non-assignable cause (state-of-the-art failure modes), are also provided. Parameter estimates for each of the models can be obtained via usual maximum likelihood procedures. Each of the derived discrete reliability-growth models is legitimate in the sense that ft accommodates monotonic growth in reliability as testing progresses through consecutive system configurations. The use of any particular discrete reliability-growth model cannot be supported by the learning-curve property, however, unless the appropriate assumptions identified in this paper are satisfied.