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Abstract

The article presents a method of mode control based on neural network fault diagnosis and evaluation of the technical condition of electrically driven gas pumping unit for subsystems for the diagnostic determination of the coefficient that takes into account the manifestation of the fault on the basis of the neural networks of the Kohonen type. The coefficients taking into account the manifestation of faults to specific subsystems allow us to identify and assess faults. The graphs of the results of the evaluation of the technical condition of electrically driven gas-pumping unit are provided with respect to the identified faults in sub-systems (lubrication system, supercharger, engine (stator winding), the motor (mechanical defects). The proposed system improves the accuracy and completeness of diagnosis for electrically driven gas-pumping unit by using neural networks of Kohonen type.

Keywords

Electrically driven gas pumping unit, control system modes, assessment of the technical condition, engine diagnostics, Kohonen neural network, factor existence of these faults.

Irina S. Babanova

Ph.D. student, Department of electrical power engineering and electromechanics, Saint-Petersburg mining University, Saint Petersburg, Russia.

Yuri L. Zhukovsky

Ph.D. (Eng.), Associate Professor, Department of electrical power engineering and electromechanics, Saint-Petersburg mining University, Saint Petersburg, Russia.

Nikolay A. Korolev

Postgraduate student, Department of electrical power engineering and electromechanics, Saint-Petersburg mining University, Saint Petersburg, Russia.

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