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Kurilin Sergey P.

Dr. Sci. (Eng.), Professor, Department of Electromechanical Systems, Branch of the National Research University "MPEI" in Smolensk
Smolensk, Russia

Fuzzy cognitive modeling of heterogeneous electromechanical systems

The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations. Read more...

A computer program for electromechanical system operational diagnostics based on the topological approach

The paper presents a method, a mathematical model, and a computer program for the operational diagnostics of an electromechanical system (EMS). During EMS operation, service aging changes the properties of the parametric matrices of the windings and, as a consequence, the characteristics of the EMS vector space. Periodic testing of the vector space offers relevant and reliable data on the current health of the EMS, its changes during operation, and the risk of loss of function. The object of the study is an asynchronous electric motor (AEM). It is urgent to automate the process of assessing the current health of an AEM and to organize the storage of information on its states at different stages of its life cycle. To solve the problem, software (SW) for accumulation of information on AEM operation and for evaluation of its basic performance metrics has been developed in the Python programming language. The SW is based on the topological approach to diagnostics, which implies the analysis of the current responses of motor rotor windings to phase voltage pulses. The SW enables one to determine the rate of the service aging of an item, the probability of its survival and residual life, to obtain access to the history of previous diagnostics, and to visualize the in-service history of the above-mentioned performance metrics. The developed SW can be used to increase the AEM operation efficiency and to plan engineering or repair work; it can also be used as an information source for re- engineering and modification of existing AEMs. The described SW can be extended to perform operational diagnostics based on the topological approach of devices of various types. Also, this SW can be considered as a separate information component of the digital twin of a complex EMS, which will allow us to study the main indicators of its reliability, fault tolerance and operational efficiency at all stages of the life cycle. Read more...

Fuzzy relational cognitive temporal models for analyzing and state prediction of complex technical systems

The effectiveness of fuzzy cognitive modeling methods for analyzing and predicting the state of complex technical systems (STS) is justified by the following reasons: significant interdependence, non-linear nature and incompleteness of information about the mutual influence of the analyzed parameters of the CTS; a variety of effects of internal and external factors on the CTS; complexity and cost of conducting experimental studies during the operation of these systems. The main limitations of fuzzy cognitive models for modeling STS dynamics are: the complexity of taking into account the mutual influence of parameters with their different time lags relative to each other; the need for their constant operational adjustment and training of component models for all parameters during the operation of the CTS. In this paper, Fuzzy Relational Cognitive Temporal Models (FRCTM) are developed. These models combine the advantages of various types of fuzzy cognitive models, and at the same time neutralize the main limitations of the analysis and prediction of the state of the CTS, which are inherent in the well- known fuzzy cognitive models. The paper also proposes models of system dynamics that take into account the specifics of the FRCTM. We have also developed an approach and implemented a method for calculating fuzzy dependencies in vector-matrix form for dynamic modeling of the CTS. The proposed method makes it possible to solve the problems of increasing the uncertainty of the results and the output of fuzzy values of the FRCTM concepts beyond the ranges of the base sets due to the execution of mass iterative computations. An example of modeling heterogeneous electromechanical systems based on FRCTM is given. The results obtained are the basis for solving a whole range of tasks of analysis, predictive evaluation, modeling of different scenarios of the functioning and development of heterogeneous electromechanical systems for various system factors, operating modes and external conditions. Read more...

Computer program for modeling of technical state indicators of electromechanical systems

The article is aimed at solving the problem of scientific justification of criteria and methods for assessing the technical state of electromechanical systems based on the topological diagnostic method. Mathematical model and computer program for simulation of technical state indices of asynchronous electric motors (AEM) are presented. Functions and Green matrices, as well as deviation matrices, are considered as such indicators. The basis of the program is the mathematical model of the AEM with a non-accelerated rotor and non-homogeneous windings. AEM is supplied from pulse voltage source. The action is carried out in different directions of the vector space of the motor in order to determine its characteristics and degree of homogeneity. Based on the reactions of the object, the program calculates and analyzes technical indicators for intact and damaged states of the AEM. A computer program for mathematical modeling of the technical state indicators of the AEM was carried out using the Maple package of symbolic and numerical calculations, which provides extensive opportunities for mathematical studies of various levels. A description of a software implementation of the proposed mathematical model is given. An example of using a program to model the performance of a serial motor with specified technical characteristics is given. The article presents the results of modeling the object indicators corresponding to the object different operational states. A reference state, a damaged state characterized by a change in the properties of the vector space during long-term operation, as well as a limit state, which corresponds to a break in one of the phases of the rotor winding, were defined as these states. Conclusions on each of the given electric motor states are given. Read more...