Skip to main content

Barkhausen Noise Tentative Analysis using Neural Networks

  • Conference paper
Computational Mechanics ’95
  • 26 Accesses

Abstract

It becomes difficult to build new nuclear power stations, so assurance of safety of existing power stations over extended period is needed. For this purpose it is essential to predict and diagnose the degree of degradation in structural materials.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Alessandro, C. Beatrice, G. Bertotti and A. Montorsi, Domain-wall dynamics and Barkhausen effect in metallic ferromagnetic materials.I.Theory, J.Appl.Phys.68(6) (1990), pp.2901–2907

    Article  Google Scholar 

  2. S. Haykin and S. Kesler, Prediction-Error Filtering and Maximum Entropy Spectral Estimation, in Nonlinear Methods of Spectral Analysis, Springer, Berlin (1979)

    Google Scholar 

  3. M. Otaka, K. Enomoto and K. Takaku, Detection of Material Damage by SQUID and Ferrofluid, Trans. of the Japan Society of Mechanical Engineers.A.No.560, Vol.59 (1993) pp.222–227

    Google Scholar 

  4. S. E. Fahlman, Faster-Learning Variations on Backpropagation: An Empirical Study, Proceedings of the 1988 Connectionist Models Summer School (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ochiai, T., Shuto, T., Hamabe, S., Yamaguchi, A., Maeda, N., Yagawa, G. (1995). Barkhausen Noise Tentative Analysis using Neural Networks. In: Atluri, S.N., Yagawa, G., Cruse, T. (eds) Computational Mechanics ’95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79654-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-79654-8_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-79656-2

  • Online ISBN: 978-3-642-79654-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics