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Volume 56 Issue 1
Jan.  2021
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Journal of Southwest Jiaotong University  > 2021  >  56(1): 190-196.
WANG Zicheng, ZHANG Yadong, GUO Jin, SU Lina, YANG Jing, SONG Ci, LI Kehong. Fault Diagnosis for Track Circuit Based on Interval Type-2 Neural-Fuzzy System[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 190-196. doi: 10.3969/j.issn.0258-2724.20180983
Citation: WANG Zicheng, ZHANG Yadong, GUO Jin, SU Lina, YANG Jing, SONG Ci, LI Kehong. Fault Diagnosis for Track Circuit Based on Interval Type-2 Neural-Fuzzy System[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 190-196. doi: 10.3969/j.issn.0258-2724.20180983
shu

Fault Diagnosis for Track Circuit Based on Interval Type-2 Neural-Fuzzy System

doi: 10.3969/j.issn.0258-2724.20180983
  • Received Date: 13 Nov 2018
  • Rev Recd Date: 17 May 2019
    • Available Online: 18 Aug 2020
  • Publish Date: 01 Feb 2021
    Abstract
  • At present, the threshold method, despite its low efficiency, has still been used to identify the fault of track circuit on site. To handle this, an interval type-2 neural-fuzzy system (IT2NFS) was built by combining neural networks and fuzzy logic. Intelligent identification of failure modes was realized by constructing a diagnostic model. During the construction of the diagnostic model, a preliminary network structure was established through the structure identification. Uniform design method was used to generate the mean values of fuzzy sets. Then the standard deviations and initial consequent parameters were generated through performing a similarity test on training samples. At last, the optimized consequent parameters were obtained by recursive singular value decomposition to reduce the output error. For 8 common failures, a total of 9000 samples were collected from the test platform. Of them, 6300 samples were used for model training, the rest 2700 samples were used for testing. The test results show that when using the IT2NFS model for fault diagnosis, the recognition rate of each fault category was above 82%, the average correct rate was 90.9%, and the simulation time was only 10.59 s.

     

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