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Description
  • Gas path analysis hols a central position in the engine condition monitoring and fault diagnostics technique. The success of gas path analysis depends mainly on the quality of the measurements obtained. This paper sets out to apply Artificial Neural Networks to provide a fast and accurate diagnostic tool for the identification of sensor faults. This method is effective even when different engines vary due to manufacturing or assembly tolerances. The network is also able to provide information of which sensor signal is degraded. Several architectures for networks were assessed to find the optimum design for the application. The engine performance was simulated by a computer program. This gave the data sets for the training and validation of the networks.
  • Gas path analysis hols a central position in the engine condition monitoring and fault diagnostics technique. The success of gas path analysis depends mainly on the quality of the measurements obtained. This paper sets out to apply Artificial Neural Networks to provide a fast and accurate diagnostic tool for the identification of sensor faults. This method is effective even when different engines vary due to manufacturing or assembly tolerances. The network is also able to provide information of which sensor signal is degraded. Several architectures for networks were assessed to find the optimum design for the application. The engine performance was simulated by a computer program. This gave the data sets for the training and validation of the networks. (en)
Title
  • Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation
  • Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation (en)
skos:prefLabel
  • Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation
  • Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation (en)
skos:notation
  • RIV/00010669:_____/10:#0001094!RIV11-MSM-00010669
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0501)
http://linked.open...iv/cisloPeriodika
  • 1
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 247390
http://linked.open...ai/riv/idVysledku
  • RIV/00010669:_____/10:#0001094
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Gas Turbine Engine; Gas Path; Diagnostics; Artificial Neural Network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [AEFDF0ACC2D0]
http://linked.open...i/riv/nazevZdroje
  • Czech Aerospace Proceedings / Letecký zpravodaj
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 1/2010
http://linked.open...iv/tvurceVysledku
  • Lamka, Jaromír
issn
  • 1211-877X
number of pages
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