ISSN 1991-2927
 

ACP № 1 (55) 2019

Author: "Evgenii Nilolaevich Egov"

Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; First Vice-Rector - Vice-Rector for Scientific Affairs of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [e-mail: jng@ulstu.ru]N. Yarushkina,

Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Postgraduate Student and Assistant at the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [e-mail: e.egov@ulstu.ru]E. Egov

The Algorithm for Identifying New Anomalies in Technical Time Series Diagnosis 000_4.pdf

The article discusses the ways to diagnose the time series in order to detect anomalies in them. The authors propose to determine the number of each point of the values of the two parameters. Also a set of situations related to changes in the values of these parameters between the points should be prepared. While analyzing series, the frequency of each situation occurrence should be determined. If the probability of situations occurrence is less than 0.01, then such situations may be attributed to an abnormal ones. On the basis of the previous situation choice, a template that allows identifying these anomalies in the future is created. As one of the pairs for situations identification, the entropy measures values obtained from fuzzy time series are proposed to use. The first measure of entropy is calculated by the value of the membership function point compared to the fuzzy label. The second measure of entropy is calculated on the basis of the deviation of the actual value trends from the forecasting one. Series analysis is performed on the basis of the second pair. This pair represent “fuzzy label - fuzzy trend” one. This pair was introduced to identify long-term stay in the areas of certain states, which can be attributed to the abnormal ones. It also describes the algorithm to identify previously unknown anomalies and search of anomalies patterns. The experiment was carried out in order to check the efficiency of the algorithm. Time series of physical quantities characterizing work of important units of helicopter engines in which it was necessary to reveal the presence of defects were investigated. The main interest of this paper is the anomaly detection algorithm based on the measure of the uncertainty of the time series. The article is intended for professionals diagnosing technical systems.

Entropy measure, diagnosis, time series, anomalies.

2016_ 2

Sections: Information systems

Subjects: Information systems.


Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First Vice-Rector - Vice-Rector for Science of Ulyanovsk State Technical University (UlSTU); graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [e-mail: jng@ulstu.ru]N. Yarushkina,

Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [e-mail: vvsh85@mail.ru]V. Voronina,

Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University with a specialty of Applied Informatics (in Economics); an author of articles in the field of intellectual analysis of time series. [e-mail: timina_i@mail.ru@ulstu.ru]I. Timina,

Evgenii Nikolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [e-mail: e.egov@ulstu.ru]E. Egov

Forecasting Technical System State With the Application of Entropy Measure for Fuzzy Time Series Diagnosis 000_6.pdf

This article discusses the ways to forecast time series of technical systems on the basis of the hypothesis of trends conservation, the hypothesis of trends stability and the hypothesis of forecasting for a specified period as well as forecasting with the use of the measure of entropy for fuzzy time series. The method of calculating the measure of entropy for fuzzy time series has been described in the previous issue of the journal. The software system of diagnosing and forecasting fuzzy time series based on the measure of entropy is also considered in the article. The system is divided into several modules, with the opportunity to use some of them in the other systems of time series prediction. The main interest of this paper is the prediction algorithm that was designed on the basis of time series measure of entropy and the comparison of the two approaches to forecasting fuzzy time series. The comparison was made on the basis of the values of MAPE, MSE, RMSE errors obtained from values of 10 rows predicted by two programs. The first program is based on the selection of one of the hypotheses, the second one described in this article is based on the prediction with the use of measure of entropy. This article is intended for professionals diagnosing technical systems.

Measure of entropy, prediction, time series.

2015_ 3

Sections: Mathematical modeling

Subjects: Mathematical modeling, Automated control systems.


Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First Vice-Rector - Vice-Rector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [e-mail: jng@ulstu.ru]N. Yarushkina,

Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series [e-mail: vvsh85@mail.ru]V. Voronina,

Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intelligent information systems. [e-mail: e.egov@ulstu.ru]E. Egov

Entropy Application to the Diagnosis of Technical Time Series 000_6.pdf

The article deals with the method for time series diagnosis based on the measure of the time series uncertainty. The formula for finding the measure of entropy for fuzzy time series is determined. The algorithm for finding the measure of entropy for fuzzy time series is of particular interest. A model of expert diagnostic rules for aircraft accessories is developed. The models of the behavior of objects such as the main gearbox and power plant engine helicopter are offered. Interpretation of natural experiment for the purpose of diagnosis of helicopter units held by analyzing the quality of the built models. A set of programs for mathematical modeling and predicting the behavior of aircraft accessories based on fuzzy measure of the uncertainty of the time series is developed. The model showed high accuracy in determining the characteristics of the time series and the identification of dangerous areas while experimenting. The developed algorithm can be successfully applied for the diagnosis and prediction of time series. This article is intended for specialists diagnosing technical systems.

Measure of entropy, diagnosis, time series.

2015_ 2

Sections: Information systems

Subjects: Information systems.


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