Defect trend analysis of Airborne Fire Control Radar using maintenance history

Younus, Bilal; Manarvi, Irfan Anjum

doi:10.1109/aero.2011.5747593

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Other existing approaches for fault detection and diagnosis based on radar B.I.T. are system modeling with a multi-signal model [19] or embedded mobile agents [20], whereas in [21] the authors analyze month wise defects data and maintenance history of airborne fire control radar subsystems over a period of two years in different locations (tropical, humid regions) in order to highlight useful conclusions for maintenance crew. Another trend analysis methodology is applied in [22] on B.I.T.…”

Section: B Possible Data-mining Toolsmentioning

confidence: 99%

Advances in production testing and operational support of new AESA radars

Kemkemian

Enderli

Larroque

2013

2013 IEEE International Symposium on Phased Array Systems and Technology

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The Active Antennas (AESA) technology has dramatically enhanced the operational capability of modern Radars because of much higher performance than the older generations but also in terms of reliability and operational availability. Nevertheless, minimizing production costs and cost of ownership of these systems remains a major industrial challenge. The paper is organized in two parts. The first one deals with improvements achieved so far for reducing the costs and the complexity of industrial testing. In the second part, a novel method for testing in production phase based on DataMining using Bayesian Networks is presented. By processing the raw flow issued from the Built-In-Test (B.I.T.), it allows detecting and diagnosing accurately defects that are difficult to catch with current methods, such as fugitive failures or initiation of defects. A first validation of the method is reported in this paper with real B.I.T. data. Originally planned for testing in production, this method could replace, in the future, the current B.I.T. which is used in operational use.

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Section: B Possible Data-mining Toolsmentioning

confidence: 99%