Advanced

A Novel Approach For Gas Turbine Condition Monitoring Combining Cusum Technique And Artificial Neural Network

Fast, Magnus LU ; Palme, Thomas and Genrup, Magnus LU (2009) 54th ASME Turbo Expo 2009 In Proceedings Of The Asme Turbo Expo 2009, Vol 1 p.567-574
Abstract
Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine's performance. The results are promising, displaying fast... (More)
Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine's performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
condition monitoring, ANN, CUSUM, gas turbine
in
Proceedings Of The Asme Turbo Expo 2009, Vol 1
pages
567 - 574
publisher
American Society Of Mechanical Engineers (ASME)
conference name
54th ASME Turbo Expo 2009
external identifiers
  • WOS:000277052900056
  • Scopus:77953213468
ISBN
978-0-7918-4882-1
DOI
10.1115/GT2009-59402
language
English
LU publication?
yes
id
4d27eec3-ca7c-4232-8b3c-ea8dfff2adb2 (old id 1616491)
date added to LUP
2010-06-23 10:23:50
date last changed
2016-10-13 04:40:03
@misc{4d27eec3-ca7c-4232-8b3c-ea8dfff2adb2,
  abstract     = {Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine's performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.},
  author       = {Fast, Magnus and Palme, Thomas and Genrup, Magnus},
  isbn         = {978-0-7918-4882-1},
  keyword      = {condition monitoring,ANN,CUSUM,gas turbine},
  language     = {eng},
  pages        = {567--574},
  publisher    = {ARRAY(0x8ac4390)},
  series       = {Proceedings Of The Asme Turbo Expo 2009, Vol 1},
  title        = {A Novel Approach For Gas Turbine Condition Monitoring Combining Cusum Technique And Artificial Neural Network},
  url          = {http://dx.doi.org/10.1115/GT2009-59402},
  year         = {2009},
}