Information technology for automated assessment of the artillery barrels wear based on SVM classifier

Abstract

An information technology for the automated assessment of the has been developed wear level has been developed. Information
technology is based on the analysis of acoustic fields accompanying a shot. The acoustic field of the shot consists of a ballistic wave
accompanying a projectile flying out at a supersonic speed, and a muzzle wave generated when propellant gases are ejected from the
barrel. The parameters of the ballistic and muzzle waves depend significantly on the level of barrel wear. This makes it possible to
construct an automatic classifier of the barrel wear level based on the analysis of informative features of acoustic signals recorded by
microphones near the weapon's firing position. The information technology is based on a binary SVM classifier. A set of records of
acoustic fields of shots was synthesized on the basis of real signals recorded when firing a 155 mm howitzer. From the set of records,
a training and test set of information features were formed for training the classifier and assessing its quality. Methods of preliminary
data normalization of training and test samples are investigated. A technique for optimizing the classifier hyperparameters with
instance cross-validation has been developed. The technique is a two-stage method for finding the optimal values of
hyperparameters. In the first stage, the search is performed on an exponential decimal grid. At the second stage, the optimal values of
hyperparameters are refined on a linear grid. A method for the binary classification of artillery barrels according to the wear level has
been formulated. Checking the classifier on a consistent test sample showed that it provides the correct classification of barrel wear
with a probability of 0.94. An information technology has been developed for classifying artillery barrels by wear level based on the
analysis of acoustic fields of shots. Information technology consists of three stages: data preparation, construction, training an
optimization of the binary SVM classifier and the operation of the binary SVM classifier. A field experiment was carried out, which
confirmed the correctness of the basic scientific and technical solutions. An automated system has been developed for classifying
wellbores by wear level