Gas path fault localization algorithms based on the pattern recognition theory are an important component of gas turbine monitoring systems. To simulate random measurement errors (noise) in description of fault classes, these algorithms usually involve theoretical random number distributions, like the Gaussian probability density function. A level of the simulated noise is determined on the basis of known information on typical maximum errors of different gas path sensors. However, not measurements themselves but their deviations from an engine baseline are input parameters for diagnostic algorithms. These deviations computed for real data have other error components in addition to simulated measurement inaccuracy. In this way, simulated and real deviation errors differ by an amplitude and distribution. Consequently, with such simulation, the performance of a diagnostic algorithm is poorly estimated, and therefore, the conclusion on algorithm efficiency may be wrong. To understand better noise peculiarities, plots of deviations of real measurements are tracked in the present paper. Additionally, possible deviation errors are surely analyzed analytically. To make noise presentation more realistic, it is proposed to extract random errors from real deviations and to integrate these errors in fault description. Finally, the effect of the new noise representation mode on gas turbine diagnosis reliability is estimated.

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