A fault diagnosis and prognosis method is developed for the fuel supply system in gas turbine engines. The engine startup profiles of the core speed (N2) and the exhaust gas temperature (EGT) collected with high speed sampling rate are extracted and processed into a more compact data set. The fuzzy clustering method is applied to the smaller number of parameters and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detects the failure successfully for all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

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