ISSN 1991-2927
 

ACP № 4 (58) 2019

The Effect of the Control Sample Volume on the Quality of Diagnostics of the Technical Object State

Dmitrii Anatolevich Zhukov, Ulyanovsk State Technical University, graduated from the Faculty of Information System and Technologies of Ulyanovsk State Technical University; Postgraduate Student of the Department of Applied Mathematics and Computer Science; an author of proceedings in the field of the statistical methods and machine learning. [e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ]D. Zhukov,

Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Mechanical Faculty of Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; an author of scientific papers in the field of reliability issues and statistical methods. [e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ]V. Kliachkin

The Effect of the Control Sample Volume on the Quality of Diagnostics of the Technical Object State 52_11.pdf

He problem of predicting the serviceability of a technical object in terms of its performance is considered. The source data are the known results of the evaluation of the state of an object based on the results of the previous operation: If the specified values of controlled indicators technical system intact or defective. Such a problem can be solved by methods of machine learning, it reduces to a binary classification of the states of the object. The quality of diagnostics can significantly depend on many factors: the method of training, the correct allocation of factors characterizing the operation of the object, the volume of the sample, and others. The work studies the effect of the control sample volume on the quality of diagnosis, estimated by the number of mis-predicted states using the cross-validation method. The tests were carried out in the Matlab package, ten different training methods were used: logistic regression, support vector method, decision tree bugging, and others. It is shown that the correct choice of the proportion of the control sample can improve the diagnostic quality to 5-7%.

Technical diagnostics, serviceability of the indicator of functioning, machine learning, control sample, cross-validation.

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