摘要：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.
关键词：Gas turbine diagnostics; gas turbine monitoring; fault identification; support vector machines; artificial neural networks
Loading...
