# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 6 Conclusions  
In this paper, ICEEMDAN is used to denoise the vibration signal of the piston pump, PCA is used to reduce the feature dimension, and the state recognition based on XGBoost is carried out to identify the wear state and category of the piston pump slipper. The experimental results show that:  
1. The ICEEMDAN can decompose the vibration signal of piston pump adaptively, improve the decomposition efficiency, and suppress phenomena of mode mixing. It is feasible to select an effective IMF component by using correlation coefficient.
2. Through time domain, frequency domain, and entropy, the deterioration process of piston pump can be tracked and identified comprehensively. The PCA is used to reduce data dimension and calculation cost, which improves the accuracy and efficiency of state identification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Feature** & **Feature** & \multirow{2}{*}{**Classification Technique**} & **Recognition** \\  & **Extraction Method** & & & **Rate (\%)** \\ \hline \multirow{4}{*}{1} & EEMD, Multi-domain & & & \\  & features (time domain, & & & \\  & frequency domain, & & & \\  & entropy) & & & \\
2 & EEMD, Multi-domain & & & \\  & features & & & \\
3 & EEMD, Multi-domain & & & XGBoost & 92.4 \\  & features & & & \\
4 & EEMD, Multi-domain & & & \\  & features & & & \\
5 & Multi-domain & & & \\  & features & & & \\
6 & ICEEMDAN, Multi- & & & \\  & domain features & & & \\  & ICEEMDAN, Multi- & & & \\  & domain features & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison for different methods.  
The average recognition accuracy of slipper wear state of piston pump based on ICEEMDAN and XGBoost is 99.7%. Compared with ANN, GBDT, and SVM algorithm, XGBoost identifies four different wear states better and saves the computing time, which highlights the advantages of XGBoost after parameter optimization in pattern recognition.  
All the authors contributed to the original idea and design of the study, to the analysis, to the drafting of the manuscript, and reading and approving the final version. Conceptualization, R.G. and Z.Z.; methodology, R.G.; validation, R.G., T.W., and Z.Z.; formal analysis, G.L. and D.G.; resources, R.G. and J.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, R.G. and Z.Z.; visualization, Z.Z.; Software, Z.Z. and T.W.; supervision, T.W.; project administration, R.G. and J.Z.; funding acquisition, R.G., J.Z., and D.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2019 YFB2005204), the National Natural Science Foundation of China (Grant No.52075469, 51675461, 11673040), the Key Research and Development Program of Hebei Province (Grant No. 19273708D), and the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (Grant No. GZKF-201922).  
The authors would like to acknowledge the support provided by Yanshan University.  
The authors declare no conflict of interest.