Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Fault Detection and Diagnosis Using Neural Network Design

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Included in the following conference series:

Abstract

In this work, a fault detection method is designed based on neural networks. The proposed method is that a neural network is built on-line for the normal mode, while other one is used to diagnose the faults. The simulation shows the effectiveness of the proposed method.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Rangwala, S., Liang, S., Dornfeld, D.: Pattern Recognition of Acoustic Emission Signals During Punch Stretching. Mechanical Systems and Signal Processing 1, 321–332 (1987)

    Article  Google Scholar 

  2. Prateepasen, A., Au, Y.H.J., Jones, B.E.: Acoustic Emission and Vibration for Tool Wear Monitoring in Single-point Machining Using Belief Nework. In: IEEE Instrumentation and Measurement Technology Conference, Hungary, pp. 1541–1546 (2001)

    Google Scholar 

  3. Isermann, R., Ayoubi, M., Konrad, H., Reiβ, T.: Model Based Detection of Tool Wear and Breakage for Machine Tools. In: Int. Conf. on Syst. Man & Cyber., Le Touquet, vol. 3, pp. 72–77 (1993)

    Google Scholar 

  4. Colbaugh, R., Glass, K.: Real-time Tool Wear Estimation Using Recurrent Neural Networks. In: Proc. of the 1995 IEEE Int. Symp. on Intelligent Contr., Monterey, USA, pp. 357–362 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kok Kiong Tan, Sunan Huang & Tong Heng Lee

Authors
  1. Kok Kiong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tong Heng Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tan, K.K., Huang, S., Lee, T.H. (2006). Fault Detection and Diagnosis Using Neural Network Design. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_54

Download citation

  • DOI: https://doi.org/10.1007/11760191_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation