## 3 Fault Diagnosis Method
### Improved AdaBoost Algorithm
It is found out from Table 9 that the overall performance on the correct rate of IAA is higher than other algorithms, approving the effectiveness of this algorithm. The next is AdaBoost, and the little inferior is the SVM algorithm. The essential cause of their similar accuracy is that AdaBoost is upgrading to weak classifier algorithm, while SVM emphasizes on largely bipartite classification. Therefore, the two algorithm effects are higher than BP neural network. BP neural network is like AdaBoost's weak classifier, whose classification result is not satisfactorily obtained, depending on its network framework. It can be summed up from the above three tables that the mean performance of the IAA is somewhat being raised, no matter from DR, FAR, or CR. What's more, the 90% CR verifies that the \begin{table}
\begin{tabular}{c c c c c} \hline
**Fault Type** & **BP** & **SVM** & **AdaBoost** & **IAA** \\ \hline
4 r/s Inner fault & 0.85 & 0.76 & 0.81 & 0.83 \\
4 r/s Outer fault & 0.72 & 0.88 & 0.92 & 0.93 \\
4 r/s Rolling fault & 0.73 & 0.76 & 0.8 & 0.95 \\ Standard inner fault & 0.81 & 0.82 & 0.89 & 0.85 \\ Standard outer fault & 0.76 & 0.81 & 0.81 & 0.88 \\ Standard rolling fault & 0.80 & 0.90 & 0.92 & 0.95 \\ Mean & 0.78 & 0.82 & 0.86 & 0.90 \\ \hline \end{tabular} BP: Back Propagation; SVM: Support Vector Machine; IAA: Improved AdaBoost Algorithm.

\end{table} 
Table 7: DR (Detection Rate) performance of different algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline
**Fault Type** & **BP** & **SVM** & **AdaBoost** & **IAA** \\ \hline
4 r/s Inner fault & 0.03 & 0.07 & 0.07 & 0.03 \\
4 r/s Rolling fault & 0.03 & 0.05 & 0.04 & 0.03 \\ Standard inner fault & 0.09 & 0.06 & 0.06 & 0.05 \\ Standard outer fault & 0.10 & 0.05 & 0.04 & 0.03 \\ Standard rolling fault & 0.07 & 0.06 & 0.05 & 0.05 \\ Mean & 0.060 & 0.053 & 0.048 & 0.038 \\ \hline \end{tabular} BP: Back Propagation; SVM: Support Vector Machine; IAA: Improved AdaBoost Algorithm.

\end{table} 
Table 8: FAR (False Alarm Rate) performance of different algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline
**Fault Type** & **BP** & **SVM** & **AdaBoost** & **IAA** \\ \hline
4r/s Inner fault & 0.83 & 0.76 & 0.78 & 0.83 \\
4r/s Outer fault & 0.8 & 0.91 & 0.92 & 0.95 \\
4r/s Rolling fault & 0.73 & 0.76 & 0.81 & 0.9 \\ Standard inner fault & 0.70 & 0.85 & 0.90 & 0.91 \\ Standard outer fault & 0.84 & 0.83 & 0.83 & 0.88 \\ Standard rolling fault & 0.85 & 0.88 & 0.91 & 0.94 \\ Mean & 0.79 & 0.83 & 0.86 & 0.90 \\ \hline \end{tabular} BP: Back Propagation; SVM: Support Vector Machine; IAA: Improved AdaBoost Algorithm.

\end{table} 
Table 9: CR (Classification Rate) performance of different algorithms. IAA is reliably superior than the other three comparative algorithms. Simultaneously, it is testified that the algorithm is applicable in the present study and provides a reliable method for fault diagnosis of the rolling bearing in the running department of a rail train. ## 5 Conclusions

Through the acquisition of vibration data of rolling train rolling bearings, the bearing fault can be predicted, which can ensure driving safety and reduce operating costs. However, rolling bearing vibration data has the characteristics of a large amount of data, nonlinearity, non-stationary, etc. Therefore, directed at the characteristics of nonlinear and non-stationary data of rail train, a complete long-term fault diagnosis method for rolling train rolling bearings based on exponential smoothing prediction and improved integrated learning algorithm is designed and applied to the measured bearing data set designed in this paper. First, the data is segmented using SWESSA, then the features through the local spectrum are extracted, and the segmentation points with spectrum analysis are combined. To enhance the anti-noise ability, the AdaBoost algorithm is improved into Imported AdaBoost Algorithm. The experimental results prove the reliability and practicability of Imported AdaBoost Algorithm.