Detail publikace

THE USE OF NEURAL NETWORK FOR DIAGNOSTICS REMANENT LIFETIME OF INSULATING MATERIAL

Originální název

THE USE OF NEURAL NETWORK FOR DIAGNOSTICS REMANENT LIFETIME OF INSULATING MATERIAL

Anglický název

THE USE OF NEURAL NETWORK FOR DIAGNOSTICS REMANENT LIFETIME OF INSULATING MATERIAL

Jazyk

en

Originální abstrakt

The paper deals with the usage of artificial intelligence (neural networks) for description the lifetime of insulating material of electric rotary machine. A state of insulating material is the main part of reliability of whole system. Up to now, we worked with data obtained by non-destructive method in laboratory condition on insulating sample. At this time, we are trying to apply the data, which were measured by non-destructive method too. The data were measured on real electric motor and they will be used for inputs of the neural network. The neural network then can predict the remanent lifetime of investigate system

Anglický abstrakt

The paper deals with the usage of artificial intelligence (neural networks) for description the lifetime of insulating material of electric rotary machine. A state of insulating material is the main part of reliability of whole system. Up to now, we worked with data obtained by non-destructive method in laboratory condition on insulating sample. At this time, we are trying to apply the data, which were measured by non-destructive method too. The data were measured on real electric motor and they will be used for inputs of the neural network. The neural network then can predict the remanent lifetime of investigate system

BibTex


@inproceedings{BUT18414,
  author="Miloš {Hammer} and Petr {Latina} and Zbyněk {Říha}",
  title="THE USE OF NEURAL NETWORK FOR DIAGNOSTICS REMANENT LIFETIME OF INSULATING MATERIAL",
  annote="The paper deals with the usage of artificial intelligence (neural networks) for description the lifetime of insulating material of electric rotary machine. A state of insulating material is the main part of reliability of whole system. Up to now, we worked with data obtained by non-destructive method in laboratory condition on insulating sample. At this time, we are trying to apply the data, which were measured by non-destructive method too. The data were measured on real electric motor and they will be used for inputs of the neural network. The neural network then can predict the remanent lifetime of investigate system",
  booktitle="UBR-CORR Study and Control of Corroslon in the Perspective of Sustalnable Development of Urban Distribution Grids",
  chapter="18414",
  howpublished="print",
  year="2005",
  month="june",
  pages="163--165",
  type="conference paper"
}