Using Artificial Intelligence to Determine the Type of Rotary Machine Fault

Autor:	Daniel Zuth, Tomas Marada
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Vibrodiagnostics Neuron Network Classification Learner Machine Learning Matlab Industry 4.0 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Mendel, Vol 24, Iss 2 (2018)
Druh dokumentu:	article
ISSN:	1803-3814 2571-3701
DOI:	10.13164/mendel.2018.2.049
Popis:	The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classication learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/35f7a0fd7bb64b09b9d65c417b918272 Zobrazit plný text záznamu View record in DOAJ