Fault diagnosis of train control on-board equipment based on fault text

The research focus of this paper is the on-board equipment of the CTCS 300T train operation control system, and a fault diagnosis method for train control on-board equipment based on Bayesian network is proposed. Firstly, to address the issue of imbalanced distribution of fault types in fault text, we have developed a Three-way Oversampling (3WOS) algorithm to automatically generate subclass text vector data. To tackle the problem of multiple synonyms and single semantics in fault text, we utilize Supervised Latent Dirichlet Allocation (SLDA) to conduct semantic clustering and feature analysis on the fault tracking table, and combine expert knowledge to establish a comprehensive fault information database. Then, we employ the K2 algorithm to train and integrate the collected fault information for building a Bayesian network. Finally, diagnostic reasoning is conducted using actual cases from high-speed railway operation sites of railway bureaus, and experimental results validate that our model exhibits high accuracy and feasibility.