Search Results

Two recent enhancements to Distributed Heterogeneous Simulation (DHS) are variable communication rates and higher-order predictors. Variable communication automatically controls the communication interval between any two subsystems in an attempt to achieve a desired accuracy during transient periods and maximize speed during steady-state periods. Higher-order predictors can better estimate the values of exchanged variables between data exchange instances, which can improve accuracy and possibly require fewer exchanges. A comparison between a single-computer simulation of an aircraft electric power system and an equivalent three-computer DHS show that the variable communication technique enables more accuracy and higher speed distributed simulations than fixed-step communication. In addition, higher-order predictors are shown to increase accuracy in some cases.

Future more electric aircraft (MEA) architectures that improve electrical power system's (EPS's) source and load utilization will require advance stability analysis capabilities. Systems are becoming more complex with bidirectional flows from power regeneration, multiple sources per channel and higher peak to average power ratios. Unknown load profiles with large transients complicate common stability analysis techniques. Advancements in analysis are critical for providing useful feedback to the system integrator and designers of multi-source, multi-load power systems. Overall, a framework for evaluating stability with large displacement events has been developed. Within this framework, voltage transient bounds are obtained by identifying the worst case load profile. The results can be used by system designers or integrators to provide specifications or limits to suppliers. Subsystem suppliers can test and evaluate their design prior to integration and hardware development.

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.