A Fault Tracing Method Based on Knowledge Graph and Weighted Path Ranking Algorithm

The structure of railway systems is complex, and accurately and rapidly identifying the root cause of faults post-occurrence is an emerging challenge in the industry. However, railway fault tracing mainly relies on manual experience and traditional data analysis techniques, with most tracing methods suffering from low efficiency and insufficient accuracy. To address these issues, this paper proposes a railway safety equipment fault tracing method based on knowledge graphs and weighted path ranking algorithm. The approach integrates knowledge graphs with Bayesian networks, attention mechanisms, and graph computation, utilizing a weighted calculation to run the weighted path ranking algorithm. Experimental results demonstrate that the proposed method achieves excellent tracing performance in the field of railway equipment.