The third stage was the development of a theory of mechanical reliability, which grew naturally out of the study of mechanical systems subject to uncertainties. Traditionally, reliability has been studied using probabilistic tools, focussing on the question: what is the probability of mission accomplishment? However, many of the uncertainties in mechanical systems – geometrical imperfections, time-varying loads, material properties – arise due to unfamiliar environments, new materials or new uses of existing technology. Probability distributions can describe these uncertainties, but verifying a probability distribution may be arduous, and the subsequent analysis can also be very difficult. The disparity between what is known and what needs to be known is an information-gap, and by this time, the info-gap methodology for modelling and managing uncertainty was well developed, and ready to be extended to the field of reliability. The designer needs to choose between design alternatives in order to confidently satisfy performance specifications. The basic info-gap robustness question is: how wrong can the models and data be, and the design in question will still satisfy the performance requirements? A design which performs adequately over a large range of error is preferred over a design which performs adequately only if the design-base knowledge is nearly correct. Large robustness suggests more confidence in the design. In this way the info-gap robustness leads to a prioritization of the design options. Furthermore, this can be done without knowing a probability distribution.
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