Advertisement

Application of artificial neural networks in sonic diagnosis of cracking hammer with artificial diamond

  • Li Kai-yang
  • Hu Yao-gai
  • Zhong Yu-ning
Article
  • 21 Downloads

Abstract

On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond by means of with time series analysis and time domain statistics, three-layer artificial neural network is trained by an improved BP algorithm. The results state that the fault sonic signals can be identified by trained network system precisely.

Key words

time series analysis artificial neural networks sonic diagnosis 

CLC number

TN911.6 

Document code

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    YAN Ting-hu. Application of BP neural networks in fault diagnoses of circumrotate machine[J].Southeast University Journal, 1993,23(5): 16–20 (Ch).Google Scholar
  2. [2]
    LUO Qiang. The methods of neural networks in fault diagnoses[J].Reliability and Environment Testing of Electron Manufacture, 1996, (2): 1–4 (Ch).MathSciNetGoogle Scholar
  3. [3]
    LI Kai-yang, HU Yao-gai, ZHONG Yu-ning. The fault sonic diagnosis of the pressure hammer with artificial diamond[J].Wuhan University Journal of Nat Sci, 1999,4(1): 150–154.Google Scholar
  4. [4]
    HU Yao-gai, LI Kai-yang, ZHGON Yu-ning. An improved BP algorithm of neural networks neural networks[J].Journal of Wuhan University (Natural Science Edition), 1999,45(1): 25–29 (Ch).Google Scholar

Copyright information

© Springer 1999

Authors and Affiliations

  • Li Kai-yang
    • 1
  • Hu Yao-gai
    • 1
  • Zhong Yu-ning
    • 2
  1. 1.Department of Analysis-Measurement ScienceWuhan UniversityWuhanChina
  2. 2.Department of Mechanical EngineeringHubei Polytechnic UniversityWuhanChina

Personalised recommendations