Gas turbine component fault detection from a limited number of measurements

Abstract

A method for detecting faults in the components of gas turbines, based on the use of non-linear engine models and optimization techniques, is presented. The method determines deviations in mass flow capacity and efficiency of individual engine components through minimization of appropriate cost function, formulated such that measurements are matched in an optimum way. Component performance deviations are expressed through appropriate modification factors, which are used as health parameters. The modification factors are coupled to a non-linear engine performance model and can represent different health conditions of the engine. The problem of fault diagnosis is formulated as the problem of determining the values of these factors from a given set of measurement data. The novel aspect of the method presented in this paper is that it can be used to determine health factors that are less, equal or larger in number than the available performance measurements. When measurements are fewer than the parameters to be determined, solutions are derived using an approach of the maximum likelihood type. It is demonstrated than such a solution can provide successful diagnosis for the majority of fault types expected to occur in an engine. The method presented is substantiated by application to a large bypass ratio, partially mixed, turbofan, typical of the large civil aircraft engine configuration in today's aircrafts. An extensive set of component faults is studied, representing malfunctions expected to occur in practice. The method is shown to perform successfully in fault identification over this set, using a limited number of measurements representative of current onboard instrumentation.