Technical State Prediction of Electronic Systems with Adaptive Parametric Models

Authors: Tonoyan S.A., Baldin A.V., Eliseev D.V. Published: 06.12.2016
Published in issue: #6(111)/2016  
DOI: 10.18698/0236-3933-2016-6-115-125

Category: Informatics, Computer Engineering and Control | Chapter: Elements and Devices of Computer Engineering and Control Systems  
Keywords: lime series, prediction, trend, random error, parameter, measurement, interpolation, weight coefficient, small sample, extrapolation, prior information, adaptive model, adequate model

This article provides a comparative analysis of existing methods for predicting time series, for the purpose of their applying for early detection of defects and determination of the technical state of complex systems at the current time and in future. The defect forecast and timely assessment of technical state for the subsequent period can improve the readiness and effectiveness of the system functioning as a whole, underscoring the relevance of the proposed approach to building a forecast model. Findings of the research show that the forecast model, built on the basis of the information model, makes it possible to more accurately determine the dynamics of the processes in technical systems, as this model can adequately reproduce the data regularities. If we consider the controlled parameters characterizing the state of the system as a time function, we can solve the problem of predicting changes in system state. This paper proposes an approach to build an adaptive forecasting model of system technical state. This enables us to ensure higher accuracy both of interpolation models construction and extrapolation, given the data significance in the time series using weight coefficients.


[1] Brillinger D.R. Time series data analysis and theory. Berkeley, Holt, Rinehart, and Winston, 1975. 540 p. (Russ. ed.: Vremennye ryady. Obrabotka dannykh i teoriya. Moscow, Mir Publ., 1980. 532 p.)

[2] Bendat J., Persol A. Measurement and analysis of random data. New York, Wiley, 1966. 390 p. (Russ. ed.: Prikladnoy analiz dannykh. Moscow, Mir Publ., 1989. 540 p.). Available at: http://engjournal.ru/catalog/it/hidden/1333.html

[3] Paklin N.B., Oreshkov V.I. Biznes analitika: ot dannykh k znaniyam [Business analytics: from data to knowledge]. Sankt-Petersburg, Piter Publ., 2013. 704 p.

[4] Barsegyan A.A. Analiz dannykh i protsessov [Data and process analysis]. Sankt-Petersburg, BKhV-Peterburg Publ., 2009. 512 p.

[5] Tonoyan S.A. Razrabotka struktury avtomatizirovannoy sistemy upravleniya tekhnicheskim sostoyaniem ob’’ektov aviatsionnoy tekhniki i prognoziruyushchego kontrolya. Avtoref. kand. tekhn. nauk [Automated control system structure development for object technical state control and predict control. Kand. tech. sci. abstract]. Moscow, Bauman MSTU Publ., 1985. 16 p.

[6] Chetyrkin E.M. Statisticheskie metody prognozirovaniya [Statistical forecasting methods]. Moscow, Statistika Publ., 1977. 200 p.

[7] Tonoyan S.A., Saraev D.V. Temporal database models and their properties. Inzhenernyy zhurnal: nauka i innovatsii [Engineering Journal: Science and Innovation], 2014, iss. 12 (in Russ.). DOI: 10.18698/2308-6033-2014-12-1333 Available at: http://engjournal.ru/eng/catalog/it/hidden/1333.html

[8] Vlasova E.A. Ryady [Series]. Moscow, Bauman MSTU Publ., 2006. 616 p.

[9] Draper N., Smith H. Applied regression analysis. New York, Wiley. (Russ. ed.: Prikladnoy regressionnyy analiz. Moscow, Finansy i statistika Publ., 1987. 343 p.)

[10] Maindonald J.H. Statistical Computation. New York, John Wiley & Sons. (Russ. ed.: Vychislitel’nye algoritmy v prikladnoy statistike. Moscow, Finansy i statistika Publ., 1988. 349 p.)

[11] Baldin A.V., Tonoyan S.A., Eliseev D.V. Analysis of temporal data storage redundancy by means of RDBMS. Inzhenernyy zhurnal: nauka i innovatsii [Engineering Journal: Science and Innovation], 2014, iss. 4. DOI: 10.18698/2308-6033-2014-4-1273 Available at: http://engjournal.ru/eng/catalog/it/hidden/1273.html

[12] Kendall M.G. Time series. London, Griffin, 1976. (Russ. ed.: Vremennye ryady, Moscow, Finansy i statistika Publ., 1981. 329 p.).