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Detection and interactive isolation of faults in steam turbines to support maintenance decisions

Karlsson, Christer; Arriagada, Jaime LU and Genrup, Magnus LU (2008) In Simulation Modelling Practice and Theory 16(10). p.1689-1703
Abstract
The maintenance of steam turbines is expensive, particularly if dismantling is required. A concept for the provision Of Support for the maintenance engineer in determining steam turbine status in relation to the recommended maintenance interval is presented here. The concept embodies an artificial neural network which is conditioned to recognise patterns known to be related to faults. The faults Simulated are not known to be recognized on-line and the concept is in an early stage of development, An example of a Bayesian network structure containing expert knowledge is proposed to be used, in a dialogue with the operator, to isolate the root causes of a number Of fault types. The aim is to be well informed about the statue of the turbine in... (More)
The maintenance of steam turbines is expensive, particularly if dismantling is required. A concept for the provision Of Support for the maintenance engineer in determining steam turbine status in relation to the recommended maintenance interval is presented here. The concept embodies an artificial neural network which is conditioned to recognise patterns known to be related to faults. The faults Simulated are not known to be recognized on-line and the concept is in an early stage of development, An example of a Bayesian network structure containing expert knowledge is proposed to be used, in a dialogue with the operator, to isolate the root causes of a number Of fault types. The aim is to be well informed about the statue of the turbine in order to take earlier and better informed maintenance actions. The detection procedure has been validated in a Simulation environment. (C) 2008 Elsevier B.V. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Decision support, Bayesian network fault isolation, Steam turbine maintenance, Artificial neural network fault detection
in
Simulation Modelling Practice and Theory
volume
16
issue
10
pages
1689 - 1703
publisher
Elsevier
external identifiers
  • wos:000261701200013
  • scopus:55249123555
ISSN
1569-190X
DOI
10.1016/j.simpat.2008.08.013
language
English
LU publication?
yes
id
085f177a-7278-49d9-b019-72760fdb0fdf (old id 1377527)
date added to LUP
2009-04-24 09:56:18
date last changed
2017-10-01 04:30:17
@article{085f177a-7278-49d9-b019-72760fdb0fdf,
  abstract     = {The maintenance of steam turbines is expensive, particularly if dismantling is required. A concept for the provision Of Support for the maintenance engineer in determining steam turbine status in relation to the recommended maintenance interval is presented here. The concept embodies an artificial neural network which is conditioned to recognise patterns known to be related to faults. The faults Simulated are not known to be recognized on-line and the concept is in an early stage of development, An example of a Bayesian network structure containing expert knowledge is proposed to be used, in a dialogue with the operator, to isolate the root causes of a number Of fault types. The aim is to be well informed about the statue of the turbine in order to take earlier and better informed maintenance actions. The detection procedure has been validated in a Simulation environment. (C) 2008 Elsevier B.V. All rights reserved.},
  author       = {Karlsson, Christer and Arriagada, Jaime and Genrup, Magnus},
  issn         = {1569-190X},
  keyword      = {Decision support,Bayesian network fault isolation,Steam turbine maintenance,Artificial neural network fault detection},
  language     = {eng},
  number       = {10},
  pages        = {1689--1703},
  publisher    = {Elsevier},
  series       = {Simulation Modelling Practice and Theory},
  title        = {Detection and interactive isolation of faults in steam turbines to support maintenance decisions},
  url          = {http://dx.doi.org/10.1016/j.simpat.2008.08.013},
  volume       = {16},
  year         = {2008},
}