OPTIMIZATION OF PARAMETERS OF MACHINE LEARNING OF THE SYSTEM OF FUNCTIONAL DIAGNOSTICS OF THE ELECTRIC DRIVE OF A SHAFT LIFTING MACHINE - Dovbysh, A. S.; Velykodnyi, D. V.; Protsenko, O. B.; Zimovets, V. I. (Radio Electronics, Computer Science, Control, 2018)
Relevance. The actual task of increasing the functional efficiency of machine learning of the system of functional diagnosis of the electric drive of a hoisting mine machine is solved. The specific objective of this study was to develop a method for the information synthesis of a learning system for the functional diagnosis of the electric drive of a hoisting mine machine, which allows increasing the reliability and efficiency of diagnostic solutions in accordance with the decisive rules built in the process of machine learning. Method. The method of information-extreme machine learning of the system of functional diagnosis of the electric drive of a minehoisting machine is proposed, based on the maximization of the information capacity of the system in the process of its training. Based on the computer-generated optimal learning parameters of the hyperspherical containers of the recognition classes, within the framework of the geometric approach, decisive rules that are practically invariant to the spatiality of the space of diagnostic features are constructed. In addition, increasing the efficiency of machine learning systems is achieved by parallel-sequential optimization of control tolerances for diagnostic features. In this case, the quasi-optimal control tolerances for diagnostic tests obtained during parallel optimization are used as start-ups for their sequential optimization. As a criterion for optimizing the parameters of machine learning, the modified information measure of Kulbak is used, which is a functional of the accuracy characteristics of diagnostic solutions. Results. The algorithmic and software for machine learning of the system for functional diagnosis of the electric drive of a mine hoisting machine has been developed, which makes it possible to build decisive rules for the adoption of highly reliable diagnostic solutions when the system is in operation. Conclusions. The results of physical modeling confirm the operability of the proposed method of machine learning and the developed software of the functional diagnosis system of the electric drive of a hoisting mine machine, which allows them to be recommended for solving practical problems of diagnosing and automatic control of traction machines.