Abstract
An approach for the modeling and evaluation of reliability and availability of systems using the knowledge of the reliability growth of their components is presented. Detailed models of reliability and availability for single-component systems are derived under much weaker assumption than usually considered. These models, termed knowledge models, enable phenomena to be precisely characterized, and a number of properties to be deduced. Since the knowledge models are too complex to be applied in real life for performing predictions, simplified models for practical purposes (action models) are discussed. The hyperexponential model is presented and applied to field data of software and hardware failures. This model is shown to be comparable to other models as far as reliability of single-component systems is concerned: in addition, it enables estimating and predicting the reliability of multicomponent systems, as well as their availability. The transformation approach enables classical Markov models to be transformed into other Markov models which account for reliability growth. The application of the transformation to multicomponent systems is described. >
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