Abstract
The article describes the principles of creating a hybrid prediction model and a comprehensive diagnosis of malfunctions of CNC ma-chine tools. It was proposed to improve the configuration of the diagnostic system and in-clude a neuro-fuzzy network with a dynamic Bayesian network algorithm and a particle filter in it in order to provide earlier and accurate prediction of faults. This will make it possible to predict some malfunctions in the initial stages of the operation of CNC machines, when cost-effective measures can be taken to avoid serious malfunctions or damage
Highlights
Kozlov Alexander Mikhailovich, doctor of technical sciences, professor, head of the department of technology of mechanical engineering of Lipetsk State Technical University (RF)
The article describes the principles of creating a hybrid prediction model and a comprehensive diagnosis of malfunctions of CNC machine tools
It was proposed to improve the configuration of the diagnostic system and include a neuro-fuzzy network with a dynamic Bayesian network algorithm and a particle filter in it in order to provide earlier and accurate prediction of faults
Summary
Козлов Александр Михайлович, д.т.н., профессор, заведующий кафедрой технологии машиностроения Липецкого государственного технического университета (РФ). В статье описываются принципы создания гибридной модели прогнозирования и комплексной диагностики неисправностей металлорежущих станков с ЧПУ. It was proposed to improve the configuration of the diagnostic system and include a neuro-fuzzy network with a dynamic Bayesian network algorithm and a particle filter in it in order to provide earlier and accurate prediction of faults. This will make it possible to predict some malfunctions in the initial stages of the operation of CNC machines, when cost-effective measures can be taken to avoid serious malfunctions or damage. Ключевые слова: СТАНОК С ЧПУ, НЕИСПРАВНОСТЬ, КОМПЛЕКСНАЯ ДИАГНОСТИКА И ПРОГНОЗИРОВАНИЕ, ЭКСПЕРТНАЯ СИСТЕМА
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