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As aircraft structures begin to age (that is, as flight hours accumulate), existing subcritical cracks or new cracks can grow in some high-stress points of the structural components. The usual approach is to inspect the structures periodically. Thus, a catastrophic accident during flight can be avoided. The problem then arises of choosing a sequence of inspection times which avoids both too many inspections, which may be costly, and too few inspections, that may also be costly due to a crack in an aircraft structure component not being detected for a long period. In this paper, a simple approach is proposed, where after each inspection (if a crack is not detected), we choose the next inspection point so that a crack may occur within an interval between successive inspection times with a given probability. It allows one to find the inspection policies for detection of initial cracks in critical structural components of aircraft under the assumption that the parameter values of the underlying distributions are unknown; this constraint is often met in practice. Furthermore, obtaining inspection schedules under crack propagation is considered. To illustrate the proposed technique based on ancillary statistics, numerical examples are given. [ABSTRACT FROM AUTHOR]

One of the most important problems in fatigue analysis and design of aircraft structures is the prediction of fatigue crack growth in service. Available in-service inspection data for various types of aircraft indicate that the fatigue crack damage accumulation in service involves considerable statistical variability. In this paper, we consider the problem of estimating the minimum time to crack initiation (or warranty period) for a number of aircraft structural components, before which no cracks (that may be detected) in materials occur, based on the results of previous warranty period tests on the structural components in question. This problem is a special case of a general class of problems concerned with the analysis of fatigue crack damage accumulation in aircraft service. The technique proposed here for solving this problem emphasizes pivotal quantities relevant for obtaining ancillary statistics. Attention is restricted to invariant families of distributions. Numerical examples are given. [ABSTRACT FROM AUTHOR]