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Research on Module-level Fault Diagnosis of Avionics System Based on Residual Convolutional Neural Network

Authors:

Yuhang Huang,

Linlin Shi,

Yucheng LuAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 104, Pages 1 - 7

https://doi.org/10.1145/3508546.3508650

Published: 25 February 2022 Publication History

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Abstract

This paper simulates the module-level soft fault signal of the avionics system by means of fault injection, selects the CPLD power supply voltage AC value as the analysis object, and samples through the sliding window to obtain the data set. Based on the residual neural network to improve,1*1 convolution and global average pooling layer are introduced. It is verified on the data set and compared with the traditional fully connected neural network and one-dimensional convolutional neural network. The experiment proves that the proposed fault diagnosis method based on residual convolutional neural network (Res-CNN) has achieved significant improvement.

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Cited By

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Du QSheng MYu LZhou ZTian LHe S(2024)Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution NetworkEntropy10.3390/e2607055026:7(550)Online publication date: 27-Jun-2024
https://doi.org/10.3390/e26070550
Yu LZhou ZHuang YLiu JHe SYu PMeng LHong D(2023)Fault Diagnosis Algorithm for Power Module Based on A Hybrid Attention Network2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)10.1109/PHM-Hangzhou58797.2023.10482728(1-4)Online publication date: 12-Oct-2023
https://doi.org/10.1109/PHM-Hangzhou58797.2023.10482728
Liu PTian BLiu XGu SYan LBullock LMa CLiu YZhang W(2022)Construction of Power Fault Knowledge Graph Based on Deep LearningApplied Sciences10.3390/app1214699312:14(6993)Online publication date: 11-Jul-2022
https://doi.org/10.3390/app12146993

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Published In

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 February 2022

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ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Overall Acceptance Rate 173 of 395 submissions, 44%

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Du QSheng MYu LZhou ZTian LHe S(2024)Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution NetworkEntropy10.3390/e2607055026:7(550)Online publication date: 27-Jun-2024
https://doi.org/10.3390/e26070550
Yu LZhou ZHuang YLiu JHe SYu PMeng LHong D(2023)Fault Diagnosis Algorithm for Power Module Based on A Hybrid Attention Network2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)10.1109/PHM-Hangzhou58797.2023.10482728(1-4)Online publication date: 12-Oct-2023
https://doi.org/10.1109/PHM-Hangzhou58797.2023.10482728
Liu PTian BLiu XGu SYan LBullock LMa CLiu YZhang W(2022)Construction of Power Fault Knowledge Graph Based on Deep LearningApplied Sciences10.3390/app1214699312:14(6993)Online publication date: 11-Jul-2022
https://doi.org/10.3390/app12146993
Fan YZhou JYang XXie ZTang D(2022)Fault Diagnosis of Civil Aircraft Avionics System Based on Bayesian Network and Function Ground Test2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)10.1109/ICSMD57530.2022.10058309(1-4)Online publication date: 30-Nov-2022
https://doi.org/10.1109/ICSMD57530.2022.10058309

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Improved inception-residual convolutional neural network for object recognition

Fault Diagnosis of Avionics System Based on Improved CNN and BiLSTM

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