A Novel Approach For Gas Turbine Condition Monitoring Combining Cusum Technique And Artificial Neural Network
(2009) 54th ASME Turbo Expo 2009 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:
https://lup.lub.lu.se/record/1616491
- author
- Fast, Magnus LU ; Palme, Thomas and Genrup, Magnus LU
- organization
- publishing date
- 2009
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- condition monitoring, ANN, CUSUM, gas turbine
- host publication
- 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
- conference location
- Orlando, FL, United States
- conference dates
- 2009-06-08 - 2009-06-12
- 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
- 2016-04-04 10:26:14
- date last changed
- 2022-03-15 21:44:02
@inproceedings{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}}, booktitle = {{Proceedings Of The Asme Turbo Expo 2009, Vol 1}}, isbn = {{978-0-7918-4882-1}}, keywords = {{condition monitoring; ANN; CUSUM; gas turbine}}, language = {{eng}}, pages = {{567--574}}, publisher = {{American Society Of Mechanical Engineers (ASME)}}, 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}}, doi = {{10.1115/GT2009-59402}}, year = {{2009}}, }