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The gas turbine is the most common engine used in the majority of commercial aircraft. Regarding the economic and social importance of aviation, methods that can identify faults in gas turbines with precision are relevant. Aiming to detect and classify the gas turbine faults, this work uses the upper and lower singleton type-2 fuzzy logic system trained by steepest descent method. Succeeding the model presentation, comparisons are performed with other models proposed in the literature to diagnose gas turbine faults. The investigated data set was obtained from the software Propulsion Diagnostic Method Evaluation Strategy, which was developed by the National Aeronautics and Space Administration for benchmarking purposes in aviation gas turbines. The experimental results show that the upper and lower singleton type-2 fuzzy logic system model had a greater detection and classification performance than the models reported in the literature.

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