eLibrary
  • Home
  • Sources
  • Search
  • About

Research on Application Methods of Network Fault Prediction Models

Yan Li, Jindai Qu, Kai Xin, Zirui Wang
World Scientific Research Journal, (2024), Vol.10, No.8, pp.83-89
Published: July 15, 2024
DOI: 10.6911/WSRJ.202408_10(8).0010
PDF: Download Full Text PDF
Abstract

With the rapid development of information technology, the internet has become an indispensable part of our daily life and work. However, the occurrence of network failures often has a significant impact on individuals, businesses, and even the country. Therefore, predicting network failures has become crucial. In recent years, machine learning and artificial intelligence technologies have achieved significant success in many fields, including network fault prediction. This article will explore the construction, application, and optimization of network fault prediction models.

Keywords: Network Failure; Prediction Model; Network Monitoring.
APA Citation: Yan Li, Jindai Qu, Kai Xin, Zirui Wang (2024). Research on Application Methods of Network Fault Prediction Models. World Scientific Research Journal, 10(8), 83-89. https://doi.org/10.6911/WSRJ.202408_10(8).0010

References

  1. Yang Qingchuan Engine Fault Detection Based on Deep Learning [D]. Hangzhou University of Electronic Science and Technology, 2021. DOI: 10.27075/dc.cnki. ghzdc.2021.000341
  2. Single Triumph Research on Engine Fault Prediction Method Based on Deep Learning [D]. Civil Aviation University of China, 2019. DOI: 10.27627/d.cnki. gzmhy.2019.000271
  3. Mei Benxiang Research on Monitoring and Management System for Vehicle Service Platform Based on MVC Mode [D]. Guangdong University of Technology, 2018
  4. Yu Zhisheng Automotive Theory [M] Version 5 Beijing: Machinery Industry Press, 2009
  5. Hu Tianjie Research on Model Based ECU Hardware in the Loop Simulation [D] Kunming: Kunming University of Technology, 2020
  6. Wang Wangyu Automotive Design [M] Version 4 Beijing: Machinery Industry Press, 2004
  7. Zhang Wenjie, Li Changlong, Hong Yu, et al. The Application of Remote Diagnosis System Based on Big Data Technology in Automobiles [J]. Automotive Digest, 2022 (4): 30-33
  8. Zhang Jiaqi Design and Implementation of a Vehicle Test Management Information System Based on Java Web [D]. Beijing: Beijing Jiaotong University, 2016
  9. Lu Shaobing. Research on Hybrid Language Programming and Its Implementation Based on Python [J]. Science and Technology Information, 2022, 20 (14): 31-33
  10. Zhang Wei. Implementation of Vehicle Detection Algorithm Based on Python [J]. Mechanical Management Development, 2019,34 (12): 258-261
  11. Cheng Xueqi, Jin Xiaolong, Wang Yuanzhuo, et al. Overview of Big Data Systems and Analysis Technology [J]. Journal of Software, 2014,25 (9): 1889-1908
  12. Zhao Li Design and Implementation of Vehicle Monitoring System Based on Big Data Processing [D]. Beijing: Beijing Jiaotong University, 2019
  13. Zeng Xianyu Optimization and Implementation of Vehicle Monitoring System Based on Big Data Technology [D]. Changchun: Jilin University, 2016
  14. Li Guojie, Cheng Xueqi. Big data research: a major strategic area for future science and technology and economic and social development - research status and scientific thinking of big data [J]. Journal of the Chinese Academy of Sciences, 2012, 27 (6): 647-657
  15. Zeng Lei. Review of Big Data Research [J]. Software Guide, 2015,14 (8): 1-2
  16. Fu Yan, Li Hongru. Research on the Digital Transformation System Architecture of Vehicle Enterprises [J]. Automotive Digest, 2022 (7): 20-26
  17. Wang Ruwei The Application of Java Programming Language in Big Data Development [J]. Electronic Technology, 2022, 51 (1): 160-161
  18. Guo Yang, Chang Yingxian. On the Application of Java Language in Computer Software Development [J]. Digital Communication World, 2022 (1): 88-90+94
  19. Zhang Jiaqi Design and Implementation of a Vehicle Test Management Information System Based on Java Web [D]. Beijing: Beijing Jiaotong University, 2016
  20. Li Jian Diagnosis of Gas Path Faults in Variable Cycle Engines Based on Deep Learning [D]. Shanghai Jiao Tong University, 2019. DOI: 10.27307/d.cnki. gsjtu. 2019. 001850
  21. Hong Jiyu Reliability analysis of aircraft engines based on deep learning [D]. Nanjing University of Aeronautics and Astronautics, 2018
  22. Zhou Yiren, Qiu Xiaolin, Guo Zhiqiang. Fault identification of engine valve mechanism based on deep learning theory [J]. Manufacturing Automation, 2017, 39 (11): 89-93
  23. Yan Bing The Application of Deep Learning in Aircraft Engine Fault Diagnosis [D]. Shanghai Jiao Tong University, 2017
  24. Ostrowski K, Birman K, Dolev D. Extensible Architecture for High Performance, Scalable, Reliable Publish Subscription Events and Notification [J] International Journal of Web Services Research, 2007, 4 (4): 18-58
  25. YANG J. Web service componentization [J] Communications of the ACM, 2003, 46 (10): 35-40
© 2026 eLibrary ยท Stand on the shoulders of giants