Skip to main content
SpringerLink
Log in

Application of Embedded Database SQLite in Engine Fault Diagnosis System

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10955))

Included in the following conference series:

  • 2225 Accesses

Abstract

In order to improve the performance of engine fault diagnosis and reduce the cost, a Embedded fault diagnosis system has been designed. This paper discusses the design and implementation of embedded database based on SQLite combined with the analysis method of fault tree.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lv, J., Xu, S., Li, Y.: Application research of embedded database SQLite. Int. Forum Inf. Technol. Appl. 2, 539–543 (2009)

    Google Scholar 

  2. Yue, K., Jiang, L., Yang, L., Pang, H.: Research of embedded database SQLite application in intelligent remote monitoring system. Int. Forum Inf. Technol. Appl. 2, 96–100 (2010)

    Google Scholar 

  3. Wang, F.C., Shen, P., Yan, X., Wang, L.D.: Study on Application of SQLite for Locomotive Fault Diagnosis System. Railway Locomotive & Car (2012)

    Google Scholar 

  4. Assaf, T., Bechta Dugan, J.: Diagnostic expert systems from dynamic fault trees. In: Reliability & Maintainability, Symposium-RAMS, pp. 444–450 (2004)

    Google Scholar 

  5. Adamson, M.S., Roberge, P.R.: The development of a deep knowledge diagnostic expert system using fault tree analysis information. Can. J. Chem. Eng. 69(1), 76–80 (2010)

    Article  Google Scholar 

  6. Ouarnoughi, H., Boukhobza, J., Olivier, P., Plassart, L., Bellatreche, L.: Performance analysis and modeling of SQLite embedded databases on flash file systems. Des. Autom. Embed. Syst. 17(3–4), 507–542 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This paper is supported by Hubei Provincial Department of Education Science and Technology Research Foundation of B2017589; College Teachers Guiding Students’ innovation and entrepreneurship training program project of 2017.

Author information

Authors and Affiliations

  1. City College of Wuhan University of Science and Technology, Wuhan College of Foreign Languages and Foreign Affairs, Wuhan, 430083, China

    Li Huang

  2. Central Southern China Electric Power Design Institute, China Power Engineering Consulting Group Corporation, Wuhan, 430070, China

    Hui Huang

Authors
  1. Li Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Huang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Wuhan University of Science and Technology, Wuhan City, China

    Xiao-Long Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, L., Huang, H. (2018). Application of Embedded Database SQLite in Engine Fault Diagnosis System. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95933-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation