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Gas turbine sensor validation through classification with artificial neural networks

Palme, Thomas; Fast, Magnus LU and Thern, Marcus LU (2011) In Applied Energy 88(11). p.3898-3904
Abstract
Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing sensors can force a plant or component into non-optimal operation, cause complete shut-down of operation or in the worst case result in damage to components. Given their importance, sensors need regular calibration and maintenance, a time-consuming and therefore costly process. In this paper a method is presented for evaluating sensor accuracy which aims to minimize the need for calibration and at the same time avoid shut-downs due to sensor faults etc. The proposed method is based on training artificial neural networks as classifiers to recognize sensor drifts. The... (More)
Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing sensors can force a plant or component into non-optimal operation, cause complete shut-down of operation or in the worst case result in damage to components. Given their importance, sensors need regular calibration and maintenance, a time-consuming and therefore costly process. In this paper a method is presented for evaluating sensor accuracy which aims to minimize the need for calibration and at the same time avoid shut-downs due to sensor faults etc. The proposed method is based on training artificial neural networks as classifiers to recognize sensor drifts. The method is evaluated on two types of gas turbines, i.e., one single-shaft and one twin-shaft machine. The results show the method is capable of early detection of sensor drifts for both types of machines as well as accurate production of soft measurements. The findings suggest that the use of artificial neural networks for sensor validation could contribute to more cost-effective maintenance as well as to increased availability and reliability of power plants. (C) 2011 Elsevier Ltd. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Sensor validation, Gas turbine, Classification, Artificial neural, network
in
Applied Energy
volume
88
issue
11
pages
3898 - 3904
publisher
Elsevier
external identifiers
  • wos:000293195500036
  • scopus:79959845186
ISSN
1872-9118
DOI
10.1016/j.apenergy.2011.03.047
language
English
LU publication?
yes
id
89e95d4b-4733-4d15-b14e-c632c159d887 (old id 2072476)
date added to LUP
2011-08-26 09:05:32
date last changed
2017-10-01 04:32:17
@article{89e95d4b-4733-4d15-b14e-c632c159d887,
  abstract     = {Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing sensors can force a plant or component into non-optimal operation, cause complete shut-down of operation or in the worst case result in damage to components. Given their importance, sensors need regular calibration and maintenance, a time-consuming and therefore costly process. In this paper a method is presented for evaluating sensor accuracy which aims to minimize the need for calibration and at the same time avoid shut-downs due to sensor faults etc. The proposed method is based on training artificial neural networks as classifiers to recognize sensor drifts. The method is evaluated on two types of gas turbines, i.e., one single-shaft and one twin-shaft machine. The results show the method is capable of early detection of sensor drifts for both types of machines as well as accurate production of soft measurements. The findings suggest that the use of artificial neural networks for sensor validation could contribute to more cost-effective maintenance as well as to increased availability and reliability of power plants. (C) 2011 Elsevier Ltd. All rights reserved.},
  author       = {Palme, Thomas and Fast, Magnus and Thern, Marcus},
  issn         = {1872-9118},
  keyword      = {Sensor validation,Gas turbine,Classification,Artificial neural,network},
  language     = {eng},
  number       = {11},
  pages        = {3898--3904},
  publisher    = {Elsevier},
  series       = {Applied Energy},
  title        = {Gas turbine sensor validation through classification with artificial neural networks},
  url          = {http://dx.doi.org/10.1016/j.apenergy.2011.03.047},
  volume       = {88},
  year         = {2011},
}