TY - JOUR
T1 - Evaluation of gas turbine diagnostic techniques under variable fault conditions
AU - Pérez-Ruiz, Juan Luis
AU - Loboda, Igor
AU - Miró-Zárate, Luis Angel
AU - Toledo-Velázquez, Miguel
AU - Polupan, Georgiy
N1 - Publisher Copyright:
© 2017, © The Author(s) 2017.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - The aim of this study is to evaluate gas path diagnostic techniques using a principle of variable structure classification applied to cover possible fault scenarios in gas turbine maintenance. This principle allows creating more versatile and realistic fault conditions relative to existing studies such as complex fault classifications, a new boundary for fault severity, and real deviation errors. The techniques analyzed are included into a special procedure that repeats a diagnostic process many times and computes for each fault class a probability of correct diagnosis. Using this probability averaged for all the classes as the evaluation criterion, the techniques are tested under the conditions of four comparative studies. The results show that (a) there is no single technique significantly outperforming all others over the full range of diagnostic conditions even if engine operating modes, fault simulation data, fault classifications, multiple-class boundaries or the scheme of deviation errors are varied; (b) the common level of diagnosis accuracy greatly depends on the fault classification used; (c) significant influence of fault severity boundary is found. The boundary proposed makes the level of accuracy much more realistic compared to simplified boundaries previously used; and (d) the use of real deviation noise in fault class description instead of simulated errors further approaches the diagnostic conditions and results to the level expected in practice.
AB - The aim of this study is to evaluate gas path diagnostic techniques using a principle of variable structure classification applied to cover possible fault scenarios in gas turbine maintenance. This principle allows creating more versatile and realistic fault conditions relative to existing studies such as complex fault classifications, a new boundary for fault severity, and real deviation errors. The techniques analyzed are included into a special procedure that repeats a diagnostic process many times and computes for each fault class a probability of correct diagnosis. Using this probability averaged for all the classes as the evaluation criterion, the techniques are tested under the conditions of four comparative studies. The results show that (a) there is no single technique significantly outperforming all others over the full range of diagnostic conditions even if engine operating modes, fault simulation data, fault classifications, multiple-class boundaries or the scheme of deviation errors are varied; (b) the common level of diagnosis accuracy greatly depends on the fault classification used; (c) significant influence of fault severity boundary is found. The boundary proposed makes the level of accuracy much more realistic compared to simplified boundaries previously used; and (d) the use of real deviation noise in fault class description instead of simulated errors further approaches the diagnostic conditions and results to the level expected in practice.
KW - Gas turbine diagnostics
KW - artificial neural networks
KW - fault identification
KW - gas turbine monitoring
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85033475368&partnerID=8YFLogxK
U2 - 10.1177/1687814017727471
DO - 10.1177/1687814017727471
M3 - Artículo
SN - 1687-8132
VL - 9
JO - Advances in Mechanical Engineering
JF - Advances in Mechanical Engineering
IS - 10
ER -