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The purpose of this study was to optimize the current design of the roll-on, roll-off sensor deployment system support arm for the C-130 Hercules. The Department of Defense (DOD) and the National Guard (NG) will be using these sensor pallet systems in a variety of command and control configurations for counter narco-terrorism applications along with several other applications. The original design for the sensor deployment arm will be drawn using CAD, and then a Finite Element Analysis will be modeled and analyzed using Pro/ENGINEER and Pro/MECHANICA. This will show the stress concentrations and the areas where weight can be saved. The most concerning variable will be the height of the mechanical arm attachment. By decreasing that height, and shortening the mechanical arm, the moments will decrease, and the required torque will be less.

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.

The electrical and mechanical failures (such as bearing and winding 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 its costs are high. This paper presents the development of feature extraction and diagnostic algorithms to ultimately 1) differentiate between these failure modes and normal aircraft operational modes; and 2) determine the degree of damage of a generator. Electrical signature analysis based features were developed to distinguish between healthy and degraded generators while taking into account their operating conditions. The diagnostic algorithms were developed to have a high fault / high-hour detection rate along with a low false alarm rate.

The electrical and mechanical failures (such as bearing and winding 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 its costs are high. This paper presents the development of several algorithms to differentiate between these failure modes and normal aircraft operational modes, determine the degree of damage and remaining life of a generator. P-3 generator data (vibrations & phase voltages/currents) were collected for a seeded bearing failure involving lubrication defects in main bearing system. The results show that the frequency domain analysis of the generator's phase voltage can be used to detect its general health and impending bearing failures.