Optimizing Automated Gas Turbine Fault Detection Using Statistical Pattern Recognition

A method enabling the automated diagnosis of gas turbine compressor blade faults, based on the principles of statistical pattern recognition, is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurement data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurement data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an industrial gas turbine while extension to other aspects of fault diagnosis is discussed.