Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

The rapid development of computer information technology has made various fault diagnosis and detection technologies emerge in an endless stream. As one of the main transportation vehicles, the detection efficiency of fault diagnosis of railway signal equipment has important practical significance for maintaining the overall safe operation of railways. On the basis of the traditional FP-Growth algorithm, improve the TF-IDF algorithm to realize the weight discretization of text features, and realize the improvement of the FP-Growth algorithm by adjusting the adaptive confidence and support. The FP-Growth algorithm will be improved. FP-Growth algorithm is used for performance tests and applications. The results show that the minimum running time-saving of the proposed algorithm is 1500ms, and the average accuracy of P@N exceeds 85%, which is higher than that of the FP-Growth algorithm (81.4%) and VSM algorithm (82.1%). The PR curve of the improved algorithm is closer to the upper right, which effectively ensures the processing of correlated data, and the overall average precision performance under the influence of positive and negative signal-to-noise ratio values exceeds 95%. And the signal curve generated by the algorithm. The error range of the data under the four fault types of track circuit, turnout, signal, and connecting line floats between 1% and 5%. The improved FP-Growth algorithm can effectively analyze railway fault types and data. Perform analysis and data processing to minimize diagnostic errors.

@article{Yang2022,
  title     = {Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Yueqin Yang},
  doi       = {10.14569/IJACSA.2022.0131279},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131279}
}