INCREASING THE EFFICIENCY OF THERMAL DIAGNOSTIC CONTROL OF ELECTRIC MOTORS

Authors

  • S. Yesaulov O. M. Beketov National University of Urban Ecоnоmy in Kharkov
  • О. Babichevа O. M. Beketov National University of Urban Ecоnоmy in Kharkov
  • M. Kovalik O. M. Beketov National University of Urban Ecоnоmy in Kharkov

Keywords:

artificial neural network, remote control, modeling, parameter converter, modulator, transport, traction motor, identification, programming

Abstract

The article considers the cause of electromechanical equipment heating (EMЕ) during its operation. It has been reflected the well-known malfunctions of electric motors, that lead to overheating and failure of their individual components. Based on the analysis of existing methods of thermal diagnostics, It has been considered the trends in the creation of devices for monitoring thermal processes and it has been noted the reasons that restrain the use of technical diagnostics tools for thermal faults during the operation of EME. The purpose of this work was to increase the efficiency of the formation of initial information messages. Using the experimental data in the implementation of the EME working cycles, value of the thermal process velocity accompanying the variable technological modes with the bipolar behavior of the output ordinate were determined. Compared to the heating temperature, the rate of value change with a more noticeable contrast reflected the thermal events in the EME, thus determining the priority of this parameter to increase the efficiency of the measuring device. It has been considered methods of forming an array of initial data using a remote transducer sensor to control the heating temperature of equipment with a modulator. It has been proposed algorithms for the electronic formation of an array of initial values and their sorting according to the “principle of flotation”, when a select variables, belonging to the heating processes or cooling of equipment, is provided. A way and an algorithm for determining the rate of temperature change based on current data using a D-shaper are considered. Experimental studies of the electronic components of the diagnostic device with the D-shaper of the initial data array elements confirmed their physical implementation possibility by hardware and software. The results of data arrays formation, taking into account digital sequences in int format with an error of ± 1 Hz, in contrast to the most controlled parameter – float with an error of ± 0.08 ° C, did not change the properties of information messages, but made it possible to reduce the requirements for a microcalculator or a computing device. The results, obtained using the proposed technical solution, confirmed the possibility of increasing the efficiency of thermal and diagnostic control, contributing to a more accurate identification of possible electric motor malfunctions in the EME. The work presents illustrations confirming the suitability of mathematical descriptions and algorithms for processing the initial data for their practical application in electronic measuring instruments for monitoring and diagnosing malfunctions based on thermal events.

Author Biographies

S. Yesaulov, O. M. Beketov National University of Urban Ecоnоmy in Kharkov

PhD in Technical Sciences, Associate Professor, Associate Professor of the Department of Electric Transport

О. Babichevа, O. M. Beketov National University of Urban Ecоnоmy in Kharkov

PhD in Technical Sciences, Associate Professor, Associate Professor of the Department of Electric Transport

M. Kovalik , O. M. Beketov National University of Urban Ecоnоmy in Kharkov

Student

References

1. Chang Liu, Shu Zhou, Xindong Liu, Can Chen (2017). The Elevator Fault Diagnosis Method Based on Sequential Probability Ratio Test (SPRT), Automation, Control and Intelligent Systems, 5(4), P.50 – 55.
2. Nosov, V. V. (2012). Diagnostics of machines and equipment: study guide. St. Petersburg : Publishing house «Lan", 384.
3. Esaulov, S. M. (2019). Control and modeling parameters for heat diagnostics of power electrical equipment failure − Urban services. Kiev: Technics, Iss. 3 (149), pp. 19 − 28.
4. Terekhov, V.M. (1987) Elements of an automated electric drive: a textbook for universities. Moscow : Energoatomizdat, 224.
5. N. Rezki1, O. Kazar, L. H. Mouss, L. Kahloul, D. Rezki1, (2017). A hybrid Approach for Complex Industrial Process Monitoring, Journal of Scientific & Industrial Research, Vol. 76, pp. 608 – 613.
6. Li J, Shi J, & Satz D, (2006). Modelling and Analysis of Disease and Risk Factors Through Learning Bayesian Network from Observational Data, Technical Report.
7. Bellini, A. Bellini, A., Filippetti, F., Tassoni, C., Capolino G. A. (2008). Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics, Vol. 55, No. 12, pp. 4109 – 4126.
8. Kruglov, V.V. (2001). Fuzzy logic and artificial neural networks. Moscow : FIZMATLIT, 201.
9. Khaikin, S. (2006). Neural networks: a full course. Moscow : Williams, 1104.
10. Esaulov, S. M. (2009). The design of components for systems for the automatic diagnosis of transport. East European Journal of Advanced Technologies. Issue 5/3 (41), рр.28 − 32.
11. Dyakonov, V.P. (2005). MATLAB 6.5 SP1 / 7 + Simulink 5/6 in mathematics and modeling. Moscow : Solon-R, 412.
12. Gutnikov, V.S. (1980). Integral electronics in measuring devices. Leningrad: Energy, 387.
13. Esaulov, S. M. (2019). Research, modeling and design of components of an artificial neural network module for remote diagnostics of electric motors. − Urban services. Kiev: Technics, Iss. 5 (151), pp. 13 − 22.
14. Semenenko, M. G. (2002). Introduction to mathematical modeling. Moscow : Solon-R, 319.
15. MATLAB. The Language of Technical Computing. Getting Started with MATLAB.The Math Works, Inc. USA, 2000.
16. Babicheva, O. F. (2018). Automated design of electromechanical devices, components of digital control systems and diagnostic systems: textbook. manual. Kharkiv : KhNUMG them. OM Beketova, 355.
17. Simulink. Model-Based and System-Based Design. Using Simulink. The Math Works, Inc. USA, 2002.
18. Biryukov, A. V. (1990). Speed measurements in microprocessor electric drives with a pulse sensor. In the book. Automated electric drive. Moscow : Energoatomizdat, 544.
19. Kharin, Y. S. (1997). Fundamentals of imitation and statistical modeling: a tutorial. Moscow : Design PRO, 288.

Published

2020-09-30

How to Cite

Yesaulov, S., Babichevа О., & Kovalik , M. (2020). INCREASING THE EFFICIENCY OF THERMAL DIAGNOSTIC CONTROL OF ELECTRIC MOTORS: Array. Municipal Economy of Cities, 4(157), 163–171. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/5650