10.14489/td.2015.12.pp.028-033

2015, 12 December

DOI: 10.14489/td.2015.12.pp.028-033

Budadin O.N., Kulkov A.A., Rykov A.N., Kozelskaya S.O., Morozova T.Yu.
FORECASTING ULTIMATE SERVICE LIFE OF COMPLEX ENGINEERING SYSTEMS BASED ON PREDICTION SIMULATION AND ARTIFICIAL INTELLIGENCE ELEMENTS
(pp. 28-33)

Abstract. A problem of evaluating the operational stability and storage and safeservice life of objects respectively (e.g. load-bearing structures of polymer composite materials) has always been important. This forecasting task is now mainly solved on the basis of the product testing as well as detailed studying laws of physical-chemical aging processes in polymer composite materials and changes in physical and mechanical characteristics of products and creating on this base corresponding test methods and mathematical predictive models. The paper states results of studying the possibility to increase the reliability of evaluating ultimate service life by building the predictive models on the basis of multifunctional systems using a wide set of various input data of, for example, accelerated tests and experimental fullscale aging, results of nondestructive testing (monitoring) the objects using artificial intelligence elements, for example, based on artificial neuron nets using abduction and palliative methods. The system to be obtained is capable of using logical approaches, selftraining in problem solution and making decisions concerning the input data information sufficiency, and determining cause-effect relations. This method is based on multicomponent multilevel information measuring systems providing the submission of timely and true aggregative (gathered as to specific characteristic parameters) information about the state, tendencies and dangerous-situation initiation signatures to be obtained at the expense of the complex treatment of data from different information, measuring, controlling and anti-emergency systems.

Keywords: operational stability, ultimate service life, prediction simulation, artificial neuron nets, artificial intelligence, cause-effect relations.

+ - About the Authors Click to collapse

O. N. Budadin, A. A. Kulkov, A. N. Rykov, S. O. Kozelskaya
Research Institute of Special Machinery, Open-End Joint Stock Company (TSNIISM JSC), Moscow, Russia. E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

T. Yu. Morozova
Engineering of the Federal State Budget Educational Higher Professional Education Institution “Moscow State Technical University of Radio Engineering, Electronics and Automatics” (MIREA)), Moscow, Russia

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