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Design of a Web-Based Computer-Aided Teaching System for Fault Diagnosis

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e-Learning, e-Education, and Online Training (eLEOT 2023)

Abstract

Traditional fault diagnosis methods usually rely on the experience and knowledge of professional technical personnel, which makes fault diagnosis often difficult and complex for non professionals. The design and research of computer-aided teaching systems aims to provide a more intuitive, interactive, and visual way to assist in fault diagnosis by combining computer technology and teaching methods. In order to improve the stability, user satisfaction, and information security of computer-aided teaching systems, a web-based fault diagnosis computer-aided teaching system is designed. Build a three-tier system structure based on B/S mode, including user interface layer, business logic layer and data access layer, focus on the design of foreground function module and background management module, so that the system has foreground and background management, online communication, data download and other functions, and select SQL Server 2000 as the system database. On the basis of hardware design, traditional genetic algorithms are improved to comprehensively consider the equality of teaching resource selection and the priority selection mechanism of local teaching resources in the selection of genetic genes, improve system work efficiency, and achieve user satisfaction in practical results. The experimental results indicate that the user satisfaction of this system is higher, and the system crash rate and personal information leakage rate are lower.

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Acknowledgements

A school-level project of Beijing Polytechnic, Project Name: Research on the application of aircraft avionics fault diagnosis system in teaching (2023X021-SXZ)

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Correspondence to Zhenchao Lei .

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Lei, Z., Li, W., Qu, M. (2024). Design of a Web-Based Computer-Aided Teaching System for Fault Diagnosis. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-031-51465-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-51465-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51464-7

  • Online ISBN: 978-3-031-51465-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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