# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 5 Degradation State Recognition with XGBoost
### Experimental Results and Analysis  
In order to better verify the efficiency and correctness of this method, K-fold cross validation [54] is used to calculate the recognition accuracy of the XGBoost model. The data set is divided into 10 parts, among which six parts were taken as training data, and the remaining four parts were taken as test data in turn for experiments. The average value of recognition accuracy is taken as the final result. Moreover, the method adopted in this paper is also compared with ANN, SVM, and GBDT shown in Table 5. It is found that, when the number of hidden layer neurons of ANN is 12, the effect is better, while the kernel function of SVM is RBF, the penalty coefficient is 4, and gamma is 0.1. By adjusting and optimizing the parameters of GBDT, \(n\) estimators = 20, max depth = 3, and min samples split (the minimum number of samples needed when the internal nodes are divided again) = 10.  
The above table shows the average accuracy and average calculation time of multiple status recognition verifications. As you can see from the table, the average recognition accuracy of the four algorithms has little difference, possibly because the feature values extracted by the method  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline Max depth & 3 \\ Min child weight & 1 \\ N estimators & 167 \\ Objective & multi: softmax \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for XGBoost.  
Figure 10: Determination of the number of trees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Method** & **Average Recognition Accuracy** & **Average Decision Time (s)** \\ \hline ANN & 0.991 & 0.094 \\ SVM & 0.997 & 0.012 \\ GBDT & 0.994 & 0.006 \\ XGBoost & 0.997 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of degradation state recognition results.  
proposed in this paper have good discrimination--among which SVM and XGBoost have the highest recognition accuracy of 0.997. SVM takes a long calculation time because of the kernel function. Thus, when the performance of the computer is the same, the average decision time of XGBoost is less than that of SVM, which is 0.03 s. Compared with the traditional GBDT model, XGBoost adds the control of model complexity and pruning processing, which makes the trained model difficult to overfitting, and the calculation time is relatively less. The average recognition rate of GBDT is 0.994, but the average decision time is lower than SVM and ANN. ANN needs to iterate repeatedly to get the ideal classification effect, so it has higher average decision time than three other algorithms. In the complex working environment, the vibration data of piston pump are complex and changeable, so it is necessary to extract a variety of characteristics from it to carry out effective diagnosis. Compared with ANN, the diagnostic accuracy of XGBoost can be improved by 0.001 to 0.013, and there is no need for a tedious parameter optimization process. The XGBoost algorithm can not only ensure high diagnosis accuracy but also reduce calculation time. In order to further prove the effectiveness of the present work in this paper, a variety of methods are introduced for comparison. The comparative results are shown in Table 6.  
The results are shown in Table : in the case of no data preprocessing, the recognition rate is low due to the influence of noise. Compared with EEMD, after ICEEMDAN preprocessing, the recognition rate will be relatively high. Therefore, the proposed method effectively identifies degraded states. It can maintain mechanical equipment through timely judgment, which has high practical value in the fault diagnosis of piston pump.