TY - GEN
T1 - A generalized fault classification for gas turbine diagnostics on steady states and transients
AU - Loboda, Igor
AU - Yepifanov, Sergey
AU - Feldshteyn, Yakov
PY - 2006
Y1 - 2006
N2 - Gas turbine diagnostic techniques are often based on the recognition methods using the deviations between actual and expected thermodynamic performances. The problem is that the deviations depend on real operating conditions. However, our studies show that such a dependency can be reduced. In this paper, we propose the generalized fault classification that is independent of the operating conditions. To prove this idea, the averaged probabilities of the correct diagnosis are computed and compared for two cases: the proposed classification and the traditional one based on the fixed operating conditions. The probabilities are calculated through a stochastic modeling of the diagnostic process, in which a thermodynamic model generates deviations that are induced by the faults. Artificial neural networks recognize these faults. The proposed classification principle has been realized for both, steady state and transient operation of the gas turbine units. The results show that the acceptance of the generalized classification practically does not reduce the diagnosis trustworthiness.
AB - Gas turbine diagnostic techniques are often based on the recognition methods using the deviations between actual and expected thermodynamic performances. The problem is that the deviations depend on real operating conditions. However, our studies show that such a dependency can be reduced. In this paper, we propose the generalized fault classification that is independent of the operating conditions. To prove this idea, the averaged probabilities of the correct diagnosis are computed and compared for two cases: the proposed classification and the traditional one based on the fixed operating conditions. The probabilities are calculated through a stochastic modeling of the diagnostic process, in which a thermodynamic model generates deviations that are induced by the faults. Artificial neural networks recognize these faults. The proposed classification principle has been realized for both, steady state and transient operation of the gas turbine units. The results show that the acceptance of the generalized classification practically does not reduce the diagnosis trustworthiness.
KW - Diagnosis trustworthiness indices
KW - Fault classification
KW - Gas turbine diagnostics
KW - Neural networks
KW - Thermodynamic model
UR - http://www.scopus.com/inward/record.url?scp=33750817323&partnerID=8YFLogxK
U2 - 10.1115/GT2006-90723
DO - 10.1115/GT2006-90723
M3 - Contribución a la conferencia
AN - SCOPUS:33750817323
SN - 0791842371
SN - 9780791842379
T3 - Proceedings of the ASME Turbo Expo
SP - 725
EP - 734
BT - Proceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air
T2 - 2006 ASME 51st Turbo Expo
Y2 - 6 May 2006 through 11 May 2006
ER -