Fault diagnosis of Integrated Core Processor in avionics system based on CNN-Transformer
ID：5
Submission ID：149 View Protection：ATTENDEE
Updated Time：2024-10-23 11:39:03
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Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
The structure and function of Integrated Core Processor (ICP) of the avionics system are relatively complex, while traditional fault diagnosis methods perform low accuracy and low efficiency. To address these issues, this paper proposes an enhanced CNN-Transformer algorithm for fault diagnosis of the integrated core processor. Initially, CNN model is employed to extract spatial features, reduce computational complexity, and preserve essential data. Subsequently, Transformer model utilizes self-attention calculations to capture relationships and characteristics within the input sequence. Finally, a fully connected neural network is applied to classify the faults. The method was validated using multiple datasets recorded by the Fiber Channel bus, achieving a diagnostic accuracy of 96.09%, outperforming other comparative approaches.
Keywords
avionics system, Integrated Core Processor， fault diagnosis, CNN-Transformer, Fiber Channel bus.
Speaker
ZhaoXinbo
Assistant Engineer Chengdu Aircraft Design and Research InstituteSubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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