AFDX network equipment fault diagnosis technology

被引:0
|
作者
Hu L. [1 ]
Liu Y. [2 ]
Guo Q. [1 ]
Shi Y. [3 ]
Ma C. [3 ]
Zhang J. [4 ]
Zhang T. [3 ]
机构
[1] General Pneumatic Department, Shenyang Aircraft Design and Research Institute, Shenyang
[2] Integrated Avionics Department, Shenyang Aircraft Design and Research Institute, Shenyang
[3] School of Software, Northwestern Polytechnical University, Xi’an
[4] School of Electronics and Information, Northwestern Polytechnical University, Xi’an
关键词
AFDX network; fault diagnosis; network equipment; network monitoring;
D O I
10.1051/jnwpu/20234130546
中图分类号
学科分类号
摘要
This paper focuses on the network equipment fault monitoring and diagnosis software, and studies the fault diagnosis of the monitored AFDX network based on the network algorithm. Firstly, the range fault characteristic parameters are designed to identify the fault type, and the correlation between the detection results and the fault characteristic parameters at each location can be obtained. Secondly, the data storage management scheme of the first level filtering and the second level caching mechanism is designed for the data collected in the detection. Then, according to the designed fault classification, fault diagnosis methods are given respectively, and the occasional anomalies are identified and suppressed. Finally, the network fault diagnosis verification module is designed, and the experimental verification is carried out from the perspectives of real-time and concurrency. The verification results prove the effectiveness of the method. ©2023 Journal of Northwestern Polytechnical University.
引用
收藏
页码:546 / 556
页数:10
相关论文
共 28 条
  • [1] ZHONG M, XUE T, DING S X., A survey on model-based fault diagnosis for linear discrete time-varying systems, Neuro-computing, 306, pp. 51-60, (2018)
  • [2] PAN M, ZHENG D, LAI X, Et al., State estimation based fault analysis and diagnosis in a receiving-end transmission system, 2022 IEEE IAS Global Conference on Emerging Technologies, pp. 1107-1112, (2022)
  • [3] MD A, FAISAL K, AHMAD I S, Et al., A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems, Industrial & Engineering Chemistry Research, 57, 32, pp. 10719-10735, (2018)
  • [4] PULIDO B, ZAMARRENO J M, MERINO A, Et al., State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems, Engineering Applications of Artificial Intelligence, 79, pp. 67-86, (2019)
  • [5] PU C, ZHOU F, LI L., Fault diagnosis method based on recursive federated transfer learning under multi rate sampling, 2021 China Automation Congress, pp. 6502-6507, (2021)
  • [6] DAI J, TANG J, HUANG S, Et al., Signal-based intelligent hydraulic fault diagnosis methods: review and prospects, Chinese Journal of Mechanical Engineering, 32, 5, (2019)
  • [7] ZHANG M, SU B, ZHAO L, Et al., User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection[J], International Journal of Information and Communication Technology, 16, 2, (2020)
  • [8] SHANG J, ZHOU D, CHEN M, Et al., Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis[J], Journal of Process Control, 77, pp. 7-19, (2019)
  • [9] LAHDHIRI H, SAID M, ABDELLAFOU K B, Et al., Supervised process monitoring and fault diagnosis based on machine learning methods, The International Journal of Advanced Manufacturing Technology, 102, pp. 2321-2337, (2019)
  • [10] CHI Y, DONG Y, WANG J, Et al., Knowledge-based fault diagnosis in industrial Internet of Things: A survey, IEEE Internet of Things Journal, 9, 15, pp. 12886-12900, (2022)