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

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.