ISSN 1991-2927
 

ACP № 4 (58) 2019

Author: "Vladimir Nikolaevich Kliachkin"

Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Science in Engineering; graduated from the Mechanical Faculty of the Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Computer Science of Ulyanovsk State Technical University; an author of articles in the field of reliability issues and statistical methods. [e-mail: v_kl@mail.ru]V. Kliachkin,

Dmitrii Anatolevich Zhukov, Ulyanovsk Branch of PJSC ‘Tupolev Design Bureau’, graduated from the Faculty of Information System and Technologies of Ulyanovsk State Technical University; a database engineer at the Ulyanovsk Branch of PJSC ‘Tupolev Design Bureau’; Postgraduate Student of the Department of Applied Mathematics and Computer Science; an author of articles in the field of the statistics methods and machine learning. [e-mail: zh.dimka17@mail.ru]D. Zhukov

Algorithm of Diagnostics of Technical Object Operation Using Aggregated Classifiers 56_4.pdf

The paper addresses the issue of prediction of technical object’s state of health using the known indicators of its operation. The basic data are the known results of the object state estimation by information about previous service: the technical system is healthy or faulty with predetermined values of specified indicators. Such problem may be solved using the machine learning methods, it reduces to binary classification of states of the object. The study showed that diagnostics quality may be improved by means of the binary classification method including aggregated approach, as well as by means of selection of the volume of the validation set and the method of selection of relevant indicators of object operation. In the example (used for the algorithm testing), F-measure value, which is the most reliable diagnostics quality measure for unbalanced classes, has increased by 6% (from 0.86 to 0.91), compared with the Support Vector Machine, and by 2%, compared with the bagging of decision trees which is the best basic method for the example considered (F = 0.89). In some cases, this 2% may be of significance from the perspective of the object operation security.

Technical diagnostics, healthy state, operation indicators, machine learning, aggregated approach, validation set, cross-validation.

2019_ 2

Sections: Information systems

Subjects: Information systems.



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: zh.dimka17@mail.ru]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: v_kl@mail.ru]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.

2018_ 2

Sections: Mathematical modeling

Subjects: Mathematical modeling.


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