Improving Radar’s Reliability and Maintainability

Approximately 30% of returned radar hardware is classified as “Cannot Duplicate” (CND), indicating a high rate of failure reports that cannot be reproduced. These returns typically result in no repairs being performed, despite extensive testing and investigation. In such cases, a repair depot is unable to replicate the failures reported by field maintenance crews. This can lead to significant waste in terms of time, cost, and resources for a company and a customer. More critically, it disrupts fighter jet operations and reduces overall operational availability. Additionally, CND returns negatively impact the reliability and maintainability (R&M) metrics of radar systems. This praxis presents the development of a machine learning algorithm that utilizes data from the Failure Reporting, Analysis, and Corrective Action System to predict CND cases. The objective is to reduce the CND return rate by 20%, thereby enhancing the R&M of fighter jet radar systems. By minimizing unnecessary hardware replacements, the proposed solution aims to improve operational availability and significantly reduce sustainment costs when implemented at the field or depot level.

Author

• Zhang, Ying Jun

Language

• en

Date created

• 2025

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Ali, Daniyal

Committee Member(s)

• Heine, Karen M
• Adetunji, Oluwatomi
• Ali, Daniyal

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/6m311q44g