PLANNING OF THE NUMBER OF TESTS WITH ZONAL ESTIMATES OF ARMAMENT AND MILITARY EQUIPMENT

Abstract

Today, the problem of reducing the cost of resources and time during planning and conducting tests remains relevant. This problem is especially acute in qualified testing agencies, where testing prototypes at different stages of the life cycle is the main functional task. Automation of planning processes and optimization of testing processes will significantly reduce such costs and improve the overall quality of testing.
 The article considers the theoretical issues of forming quantitative plans for testing samples of armaments and military equipment (AME) in obtaining zonal estimates of stochastic parameters and characteristics. The need for zonal estimates arises to confirm the quality of the sample for a given stochastic characteristic in the range from the allowable probabilistic value to the maximum (minimum) possible.
 The authors propose an approach to obtaining zonal estimates of the parameters of the armament and military equipment test sample, which theoretically meet the requirements of a given accuracy and reliability. The approach is based on the use of a flexible test plan, which is terminated when statistical confirmation of the probabilistic value not worse than specified by the developer is obtained. Ensuring the quality of evaluation is carried out by virtue of the minimum required number of repetitions of the experiment and the balance of the number of successful to the total number of experiments. It is shown that a flexible test plan can significantly reduce the total number of experiments, which will to some extent reduce the resource and time costs of testing samples of armament and military equipment.
 For the practical application of the approach of obtaining zonal estimates of the AME sample quality, its formalization and algorithmics is required. It is assumed that the developed approach will be implemented in the form of a functional module as а part of the subsystem of automated planning and quality management of AME sample tests.