# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
## 3 Experiments, Results and Discussion
### Results and Discussion  
In this paper, we developed a novel procedure for the actual data of the power plant, and obtained the expected results for the power plant. In order to further illustrate the superiority of the proposed method in this paper over other methods, it is necessary to discuss the following issues. (1) Computational efficiency. The research object of this paper was a power plant's big data, so the complexity of the algorithm was one of the important issues to be considered. The complexity of the T-SNE algorithm used in the research method of this paper is large. In general, applying \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Confusion** & \multicolumn{5}{c}{**Predicted Result (\%)**} \\ \cline{2-6}
**Matrix** & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline
0 & 97.06 & 0.08 & 1.09 & 0.67 & 0.37 & 0.73 \\
1 & 0.06 & 99.94 & 0 & 0 & 0 & 0 \\
2 & 1.24 & 0 & 98.76 & 0 & 0 & 0 \\
3 & 2.36 & 0 & 0 & 97.64 & 0 & 0 \\
4 & 0.41 & 0 & 0 & 0 & 99.59 & 0 \\
5 & 0.27 & 0 & 0 & 0 & 0 & 99.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of confusion matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Label** & **Precision** & **Recall Rate** & **F1-Score** \\ \hline
0 & 99.18\% & 96.80\% & 97.98\% \\
1 & 98.74\% & 100.00\% & 99.37\% \\
2 & 94.54\% & 99.02\% & 97.07\% \\
3 & 96.52\% & 97.63\% & 97.07\% \\
4 & 98.52\% & 99.70\% & 99.11\% \\
5 & 96.58\% & 99.72\% & 98.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Precision, recall and F1-score results. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original Data** & **by SMOTE** \\ \hline Normal & 78,513 & 78,513 \\ Fault 1 & 392 & 5832 \\ Fault 2 & 1095 & 16,823 \\ Fault 3 & 939 & 14,402 \\ Fault 4 & 1593 & 24,655 \\ Fault 5 & 1099 & 16,801 \\ Ratio & 15:1 & 1:1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The amount of fault data. the T-SNE algorithm for dimensionality reduction for a data set of millions of samples may take several hours. The number of samples calculated in this paper was about 340,000, and the training time of the model including the dimensionality reduction algorithm was less than one hour. Such computational efficiency is perfectly acceptable for an enterprise-level data application system. In addition, since the probability of serious faults in power plant enterprises is often low, we generally recommend that power plants update the training model every six months with new data, and the time to update the model here is at most a few hours. For the calculation time of the final classification model, achievement of the real-time effect can be considered (generally no more than 1 s). (2) Comparison with other algorithms. The purpose of this research paper was to develop a procedure of fault diagnosis and prediction for the power plant data. For the data dimensionality reduction algorithm, we chose the t-SNE algorithm. In the research process, we also compared a principal component analysis (PCA) algorithm at the same time. Although, the PCA method had a faster computational speed, it was still less effective than nonlinear dimensionality reduction algorithms, such as t-SNE, for complex data of the power plant due to the linear dimensionality reduction method [33]. For the final classification algorithm, in addition to the XGBoost algorithm, we also compared algorithms such as SVM and random forest (RF). From the application effect of the data in this paper, the computational results of the XGBoost algorithm and the RF algorithm were better than SVM; moreover, considering that the XGBoost algorithm borrows from RF and can support column sampling processing, which can not only reduce overfitting, but also reduce computational effort [34], the XGBoost algorithm was finally chosen in this paper. (3) Improvement of the algorithm. For a fault diagnosis and prediction system that is really applied to the power plant, the most important purpose was to be able to detect and warn about the dangerous faults in advance based on the large historical data.