Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

Authors

  • RyongSik O  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • Jiangwei Chu  School of Traffic and Transportation, Northeast Forestry University, Harbin Heilongjiang, 150040, China
  • Zhenwei Sun  School of Traffic and Transportation, Northeast Forestry University, Harbin Heilongjiang, 150040, China
  • Myongchol Ri  Institute of Science, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • MyongSu Sim  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • Yongchol Kim  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • SunGol Ryu  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • Chunlei Li  School of Traffic and Transportation, Northeast Forestry University, Harbin Heilongjiang, 150040, China
  • CholSong Hwang  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea
  • KwangBok Kim  Department of Transport Mechanical Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People's Republic of Korea

DOI:

https://doi.org/10.32628/IJSRST2183163

Keywords:

D-S Evidence Theory, Symptom Identification, Fuzzy Neural Network, RBF Neural Network, Automatic Transmission

Abstract

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

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International Journal of Scientific Research in Science and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

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How to Cite

[1]
RyongSik O, Jiangwei Chu, Zhenwei Sun, Myongchol Ri, MyongSu Sim, Yongchol Kim, SunGol Ryu, Chunlei Li, CholSong Hwang, KwangBok Kim "Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 3, pp.778-799, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRST2183163