The Fault Diagnosis Model Established Based on RVM

  • Yahui Wang
  • An YunEmail author
  • Qinghong Ye
  • Yunfeng Zhao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


This paper introduces the application of Relevance Vector Machine (RVM) in fault diagnosis. First, the theoretical contents of RVM including the algorithm characteristics, the derivation of mathematical models, the characteristics of kernel functions and multimode classification are introduced. Then, the multi-classification fault diagnosis model of building electrical system is established by using RVM. Finally, the experimental results show that the RVM model has good classification effect on small sample data.


Fault diagnosis Relevance vector machine Small sample data 



The project was partially supported by “The Fundamental Research Funds for Beijing University of Civil Engineering and Architecture”, Beijing, China with the grant No. X18191.


  1. 1.
    An, Y., Wang, Y.H., Zhang, F.C.: Research on gas pressure regulator fault diagnosis based on deep confidence network (DBN) theory. CAC (2017).
  2. 2.
    Shen, Y., Ding, S.X., et al.: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 22(22), 1567–1581 (2012)Google Scholar
  3. 3.
    Tran, V.T., Althobiani, F., Ball, A.: An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Syst. Appl. 41(9), 4113–4122 (2014)CrossRefGoogle Scholar
  4. 4.
    Ishigaki, T., Higuchi, T., Watanabe, K.: Fault detection of a vibration mechanism by spectrum classification with a divergence-based kernel. IET Signal Process. 4(5), 518–529 (2010)CrossRefGoogle Scholar
  5. 5.
    Ando, S., Kato, M.: Criticism of the Desmarais method for kernel function computation. Trans. Jpn. Soc. Aeronaut. Space Sci. 31(93), 161–170 (1988)Google Scholar
  6. 6.
    Li, C., Sanchez, R.V., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168(C), 119–127 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringBeijing University of Civil Engineering & ArchitectureBeijingChina
  2. 2.Shanghai Aerospace Energy LTDShanghaiChina

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