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Analyzing and maintaining power quality in an electrical power system (EPS) is essential to ensure that power generation, distribution, and loads function as expected within their designated operating regimes. Standards such as MIL-STD-704 and associated documents provide the framework for power quality metrics that need to be satisfied under varying operating conditions. However, analyzing these power quality metrics within a fully integrated EPS based solely on measurements of relevant signals is a different challenge that requires a separate framework containing rules for data acquisition, metric calculations, and applicability of metrics in certain operating conditions/modes. Many EPS employed throughout industry and government feature various alternating-current (ac) power systems.

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.