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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.

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.

Lithium ion batteries offer more power and longer cycle life than traditional technologies for many different applications. However, Li-ion batteries are new in the aviation applications and due to the lack of experience they are preventively removed from service before any problems may occur with the battery. This currently makes them unnecessarily expensive. A health diagnostic system needs to be developed and installed in the battery will show the state of health reducing maintenance costs. The purpose of this paper is to show Saft's approach to better understand cell aging through testing using reference electrodes. The aging of the cell anode and cathode will be analyzed under aircraft conditions to determine points of failure. The data acquired will then be used by Global Technology to create a prognostics health management (PHM) model for aviation.

An onboard Acoustic Wiring Diagnostic System to monitor the health of aircraft wiring is under development by Innovative Dynamics Inc. The AWDS incorporates passive acoustic sensors to monitor wire chafing. The system operates continuously in-flight so that intermittent wiring fault conditions can be detected as they happen. Trend analysis data can be logged to enable pro-active maintenance prior to catastrophic failure. A key advantage of the in-situ system is to perform the inspection without removing or disconnecting the wiring. Acoustic signatures of representative aircraft wiring have been characterized under simulated damage conditions. Flight ready hardware and software have been developed and flight testing is underway on an H-53 helicopter. This paper will present the wire diagnostic approach, the AWDS flight instrumentation, and some representative lab test results.

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.