Advertisement

Research on Model of Circuit Fault Diagnosis Based on Bayesian Inference

  • Fu Yu
  • Zheng Zhi-song
  • Wu Xiao-ping
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 168)

Abstract

According to the problem of information uncertainty during the process of fault diagnosis, a model of circuit fault diagnosis is proposed based on Bayesian inference. The definition of probability is extended in this model, which is explained as the subjective faith degree of experts. Besides, the knowledge process is added to the process of fault diagnosis, and the determinant rules of fault are given. Finally, the model is used in some circuit fault diagnosis, and qualitative analysis and quantitative calculation show that the model is effective and reasonable.

Keywords

Bayesian inference circuit fault diagnosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, Z., Gao, Y.: Research of Fault Diagnosis Based on Grey Theory of Multi-wavelet Entropy. Computer Measurement & Control 19(6), 1318–1324 (2011)Google Scholar
  2. 2.
    Xu, X., Wang, Y., Wen, C.: Information-fusion method for fault diagnosis based on reliability evaluation of evidence. Control Theory & Applications 28(4), 504–510 (2011)Google Scholar
  3. 3.
    Zhao, S., Liu, F.: Cross-correlation fault diagnosis in control loop based on Bayesian network. Journal of Southeast University (Natural Science Edition) 40, 277–281 (2010)Google Scholar
  4. 4.
    Zhou, Z., Ma, C., Dong, D., et al.: Auto-study diagnosis method based on Bayesian fusion. Application Research of Compute 27(5), 1764–1766 (2010)Google Scholar
  5. 5.
    Li, Y., Lu, Q., Su, W., et al.: Learning Bayesian Network from Small Scale Dataset and Application. Computer Science 38(7), 181–184 (2011)Google Scholar
  6. 6.
    Zhao, W.: Transformer fault diagnosis based on selective Bayes classifier. Electric Power Automation Equipment 31(2), 44–47 (2011)Google Scholar
  7. 7.
    Cai, Z., Sun, S., Yannou, B., et al.: Conditional Bayesian network classifier and its application in product failure rate grade indentifying. Computer Integrated Manufacturing System 16(2), 417–423 (2010)Google Scholar
  8. 8.
    He, X., Tong, X., Sun, M.: Distributed power system fault diagnosis based on Bayesian network and Dempster-Shafer Evidence Theory. Automation of Electric Power Systems 35(10), 42–47 (2011)Google Scholar
  9. 9.
    Zhang, D., Wu, S., Luo, X., et al.: Research on rapid diagnosis algorithm for complex system based on Bayesian theory. Engineering Journal of Wuhan University 44(1), 128–132 (2011)Google Scholar
  10. 10.
    Wu, Q., Wu, S., Liu, J.: Mechanical fault diagnoses approach based on Fv-SVM. Systems Engineering – Theory & Practice 30(7), 1266–1271 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Fu Yu
    • 1
  • Zheng Zhi-song
    • 1
  • Wu Xiao-ping
    • 1
  1. 1.College of Electronic EngineeringNaval Univ. of EngineeringWuhanChina

Personalised recommendations