The parametric diagnostics of gas turbine engines has been improved in the last decades due to computer technology development and better analysis methods such as artificial neural networks. It has demonstrated to be a very powerful tool providing an insight into an actual engine health condition and predicting possible future failures. On the basis of a thermodynamic model that relates monitored variables with operating conditions and fault parameters, it is possible to obtain healthy and faulted engine performances. This model allows calculating deviations between actual and baseline engine performances. Based on the deviations computed for all monitored variables, the diagnosis is made by pattern recognition techniques. These deviations include errors due to measurement uncertainty and model inadequacy. Since an engine operating point changes, the deviation errors change as well, resulting in varying diagnostic inaccuracy. In the present paper, two hypotheses on how the errors influence engine diagnosability at varying operating points are first investigated on simulated data and then verified with real information.
