Machine learning for rotating machines: simulation, diagnosis and control.

Autor:	Kornaev, Alexey, Savin, Leonid, Kornaev, Nickolay, Zaretsky, Roman, Kornaeva, Elena, Babin, Alexander, Stebakov, Ivan
Předmět:	MACHINE learning FAULT diagnosis ROTOR dynamics ROTATING machinery PATTERN recognition systems SUPERVISED learning FLUID-film bearings REINFORCEMENT learning
Zdroj:	Vibroengineering Procedia; Jun2020, Vol. 32, p223-228, 6p
Abstrakt:	The goal of this work is association of several machine learning methods in a study of rotating machines with fluid-film bearings. A fitting method is applied to fit a non-linear reaction force in a bearing and solve a rotor dynamics problem. The solution in the form of a simulation model of a rotor machine has become a part of a control system based on reinforcement learning and the policy gradient method. Experimental part of the paper deals with a pattern recognition and fault diagnosis problem. All the methods are effective and accurate enough. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu