| dc.creator |
Fentaye, Amare |
|
| dc.creator |
Ul-Haq Gilani, Syed Ihtsham |
|
| dc.creator |
Baheta, Aklilu Tesfamichael |
|
| dc.creator |
Li, Yi-Guang |
|
| dc.date |
2018-12-05T14:58:59Z |
|
| dc.date |
2018-12-05T14:58:59Z |
|
| dc.date |
2018-11-18 |
|
| dc.date.accessioned |
2022-05-25T16:40:22Z |
|
| dc.date.available |
2022-05-25T16:40:22Z |
|
| dc.identifier |
AD Fentaye, SI Ul-Haq Gilani, AT Baheta and YG Li. Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Volume 233, Issue 6, 2019, pp.786-802 |
|
| dc.identifier |
0957-6509 |
|
| dc.identifier |
https://doi.org/10.1177/0957650918812510 |
|
| dc.identifier |
http://dspace.lib.cranfield.ac.uk/handle/1826/13697 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/182549 |
|
| dc.description |
An effective and reliable gas path diagnostic method that could be used to detect, isolate, and identify gas turbine degradations is crucial in a gas turbine condition-based maintenance. In this paper, we proposed a new combined technique of artificial neural network and support vector machine for a two-shaft industrial gas turbine engine gas path diagnostics. To this end, an autoassociative neural network is used as a preprocessor to minimize noise and generate necessary features, a nested support vector machine to classify gas path faults, and a multilayer perceptron to assess the magnitude of the faults. The necessary data to train and test the method are obtained from a performance model of the case engine under steady-state operating conditions. The test results indicate that the proposed method can diagnose both single- and multiple-component faults successfully and shows a clear advantage over some other methods in terms of multiple fault diagnosis. Moreover, 5-8 sets of measurements have been used to assess the prediction accuracy, and only a 2.3% difference was observed. This result indicates that the proposed method can be used for multiple fault diagnosis of gas turbines with limited measurements. |
|
| dc.language |
en |
|
| dc.publisher |
SAGE |
|
| dc.rights |
Attribution-NonCommercial 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc/4.0/ |
|
| dc.subject |
Sensor |
|
| dc.subject |
gas turbine |
|
| dc.subject |
artificial neural network |
|
| dc.subject |
support vector machine |
|
| dc.subject |
gas path diagnostics |
|
| dc.title |
Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method |
|
| dc.type |
Article |
|