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Electrical and mechanical failures (such as bearing, winding and rotating-diode failures) combine to cause premature failures of the generators, which become a flight safety issue forcing the crew to land as soon as practical. Currently, diagnostic / prognostic technologies are not implemented for aircraft generators where repairs are time-consuming and costly. This paper presents the development of feature extraction and diagnostic algorithms to 1) differentiate between these failure modes and normal aircraft operational modes; and 2) determine the degree of damage of a generator. Electrical signature analysis (ESA) based time-domain features were developed to distinguish between healthy and degraded generators while taking into account their operating conditions. Frequency-domain based ESA techniques are used to identify the degraded components within the generators.