## 4 Discussion
### Effect Verification of Fault Visual Analysis  
In the long-term process of train operation, trains accumulate a vast amount of state monitoring data, which are large in quantity and high in dimension. Traditional relational databases of train operation data are transformed into graph databases based on triple representation via knowledge mapping technology. Triple refers to a general representation of a knowledge graph, that is G = (E, R, S), where E is the entity set in the knowledge base, R is the relation set, and S is the triple set. In this study, a train operation fault knowledge map was generated for the train operation data set, as shown in Figure 6. The structured data in our operation data set mainly included temporary repair and wheel change records, extended three-level repair records, retrofit records, advanced repair records, loading records, factory resumption information, vehicle operation fault history records, vehicle running records, advanced repair history records, vehicle operation conditions, and so forth. In the train fault data visualization method based on knowledge mapping, fault nodes were drawn as points, and fault relationship links were rendered as lines. This graphical representation can directly represent the train operation data, which is helpful for obtaining a better understanding of the entity relationship and structure information in the fault data.  
Through Figure 6, we can see that the associated maintenance information and operation information were presented intuitively based on different train entities. With the help of the generated train fault graph, the collaborative filtering analysis model was used to calculate the similarity of different train fault causes, sort the fault causes, and determine the most similar train fault causes, which can help people quickly obtain intelligent recommendations with regard to the fault causes. At the same time, the fault knowledge graph can also be used to calculate the similarity of different train faults, determine which set of train faults are caused by the same reason, and analyze correlations between faults.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Transfer Strategy**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Set** \\ \end{tabular} } & \multicolumn{8}{c}{**Train Times**} \\ \cline{3-10}  & & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline No-Transfer & Train set 1 & 21.3\% & 29.6\% & 38.5\% & 47.3\% & 48.6\% & 48.9\% & 50.1\% & 50.2\% & 49.7\% \\ No-Transfer & Train set 2 & 32.5\% & 39.8\% & 49.2\% & 56.3\% & 64.0\% & 66.1\% & 66.4\% & 67.2\% & 63.1\% \\ No-Transfer & Train set 3 & 42.6\% & 60.3\% & 65.8\% & 69.7\% & 71.3\% & 74.5\% & 75.4\% & 74.1\% & 73.2\% \\ No-Transfer & Train set 4 & 51.9\% & 63.3\% & 70.5\% & 74.0\% & 77.7\% & 78.2\% & 79.0\% & 79.2\% & 76.3\% \\ No-Transfer & Train set 5 & 63.0\% & 72.8\% & 79.1\% & 84.3\% & 86.9\% & 87.4\% & 87.1\% & 86.7\% & 84.1\% \\ No-Transfer & Train set 6 & 72.3\% & 79.1\% & 85.6\% & 87.2\% & 90.1\% & 89.9\% & 90.3\% & 89.4\% & 89.7\% \\ Fine-tuning & Train set 1 & 63.5\% & 66.7\% & 66.4\% & 67.9\% & 67.8\% & 67.9\% & 68.0\% & 68.1\% & 67.9\% \\ Fine-tuning & Train set 2 & 70.3\% & 76.6\% & 77.1\% & 78.0\% & 78.6\% & 79.2\% & 78.8\% & 78.2\% & 77.7\% \\ Fine-tuning & Train set 3 & 76.7\% & 77.7\% & 78.6\% & 79.4\% & 80.2\% & 80.1\% & 80.0\% & 79.7\% & 79.2\% \\ Fine-tuning & Train set 4 & 78.9\% & 81.