Intelligent Building Fault Diagnosis Based on Wavelet Transform and Bayesian Network

  • Jundong Fu
  • Luming Huang
  • Li Chen
  • Yunxia Qiu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


A novel fault diagnosis method is proposed in this paper in the distribution network based on wavelet transform and a Bayesian network. After the wavelet transform, decomposition, and reconstruction of various electrical basic quantities by amplitude, phase angle, and energy, the electrical fault feature quantity is combined according to various weights, and then the corresponding component switch fault characteristics are calculated by Bayesian. A simple Bayesian fusion of the electrical fault feature and component switch fault characteristics is used as the eigenvector of the Bayesian network, and then trained and predicted by Bayesian network. The experimental simulation results show that the fault diagnosis method for power distribution network based on wavelet transform and Bayesian network proposed in this paper has an obvious recognition degree according to the single fault feature. It is very accurate to identify the type and faulty components.


Intelligent building Fault diagnosis Pattern recognition Bayesian network Wavelet transform 


  1. 1.
    Dash, P.K., Samantaray, S.R.: A novel distance protection scheme using time-frequency analysis and pattern recognition approach. Int. J. Electr. Power Energy Syst. 29(2), 129–137 (2007)Google Scholar
  2. 2.
    Purkait, P., Chakravorti, S.: Wavelet transform-based impulse fault pattern recognition in distribution transformers. IEEE Trans. Power Deliv. 18(4), 1588–1589 (2003)Google Scholar
  3. 3.
    Meng, X.P., Li, J.L., Zhang, Y.W.: Fault diagnosis of building automation system based on expert system. Comput. Eng. 37(21), 273–275 + 278 (2011) (in Chinese)Google Scholar
  4. 4.
    Liu, X. R., Gao, Y. W., Wang, Z. L.: Method of Power distribution network fault diagnosis based on improved time fuzzy Petri Net. J. Northeast. Univ. (Natural Science) 37(11), 1526–1529 (2016) (in Chinese)Google Scholar
  5. 5.
    Chen, X. Z., Chen, Q., Yu, Y. J., et al.: A fault diagnosis approach of power networks based on maximum likelihood decoding Petri Net models. Trans. China Electrotech. Soc. 30(15), 46–52 (2015) (in Chinese)Google Scholar
  6. 6.
    Gao, Z. Z., Gong, Q. Y., Liu, L. J., et al.: Power system fault diagnosis based on rough set and Petri network optimized by BP algorithm. Electr. Power 49(08), 12–16 + 30 (2016) (in Chinese)Google Scholar
  7. 7.
    Xiong, G. J., Shi, D. Y., Zhu, L., et al.: Fuzzy cellular fault diagnosis of power grids based on radial basis function neural network. Autom. Electr. Power Syst. 38(05), 59–65 (2014). (in Chinese)Google Scholar
  8. 8.
    Lala, H., Karmakar, S.: Continuous wavelet transform and artificial neural network based fault diagnosis in 52 bus hybrid distributed generation system. In: IEEE Students Conference on Engineering and Systems (SCES) 2015, pp. 1–6 (2015)Google Scholar
  9. 9.
    Luo, X. H., Tong, X. Y.: Structure-variable bayesian network for power system fault diagnosis considering credibility. Power Syst. Technol. 39(09), 2658–2664 (2015) (in Chinese)Google Scholar
  10. 10.
    Zhao, X., Yang, H. G.: Utility harmonic impedance estimation based on Bayes theorem. Proc. CSEE 36(11), 2935–2943 (2016) (in Chinese)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jundong Fu
    • 1
  • Luming Huang
    • 1
  • Li Chen
    • 1
  • Yunxia Qiu
    • 1
  1. 1.School of Electrical and Automation EngineeringEast China Jiaotong UniversityNanchangChina

Personalised recommendations