Gas turbine and sensor fault diagnosis with nested artificial neural networks

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dc.contributor.author Xiradakis, N
dc.contributor.author Li, Yiguang
dc.date.accessioned 2017-09-04T14:45:56Z
dc.date.available 2017-09-04T14:45:56Z
dc.date.issued 2004
dc.identifier.citation Xiradakis N, Li YG, Gas turbine and sensor fault diagnosis with nested artificial neural networks, Proceedings of ASME Turbo Expo 2004: Power for Land, Sea, and Air, 14-17 July 2004, Vienna, Austria, Volume 7, pp. 351-359, paper number GT2004-53570 en_UK
dc.identifier.isbn 0-7918-4172-3
dc.identifier.uri http://dx.doi.org/10.1115/GT2004-53570
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/12436
dc.description.abstract Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present. Copyright © 2004 by ASME en_UK
dc.language.iso en en_UK
dc.publisher ASME en_UK
dc.rights ©2004 ASME. This is the Author Accepted Manuscript. Please refer to any applicable publisher terms of use.
dc.subject Sensors en_UK
dc.subject Gas turbines en_UK
dc.subject Artificial neural networks en_UK
dc.subject Fault diagnosis en_UK
dc.title Gas turbine and sensor fault diagnosis with nested artificial neural networks en_UK
dc.type Conference paper en_UK


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