# Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
## 6 Conclusions  
Aiming at the problem that the gear pump cannot be properly monitored and maintained in time, a mechanical equipment RUL prediction method based on the KPCA and JITL methods is proposed in this paper, which can timely warn the performance degradation process of the gear pump and timely forecast the RUL of the gear pump. It is used for the RUL prediction of gear pumps, and the prediction results show that the KPCA method can effectively extract their performance degradation indices. Simultaneously, the RUL prediction method based on JITL can effectively predict the RUL of the gear pumps. Compared to the traditional RUL prediction method, the method proposed herein exhibits a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring of axial piston pumps, gearboxes, rolling bearings, and other components, and has broad application prospects and wide applicability.  
1. For obtaining the gear pump performance degradation evaluation indices, the characteristic parameters of the pressure signal include variance, root-mean-square value, peak value, root amplitude, peak-to-peak value, absolute mean value, standard deviation, shape factor, clearance factor, and average power, which have good sensitivity to the performance degradation of the gear pump. Using the KPCA method can effectively avoid the data redundancy caused by the correlations among various characteristic parameters. With continuous performance degradation of the gear pumps, the evaluation indices present a clear monotonic increasing trend. Simultaneously, the evaluation indices have good sensitivity to the early failure performance of the gear pumps.
2. In the process of similar sample selection for the JITL method, the sample selection method based on k-VNN comprehensively considers the Euclidean distance and the angle between the information vectors, which can obviously reduce the MRE and ARE parameters of the RUL prediction method and significantly improve the prediction accuracy of the RUL prediction model. The feasibility and effectiveness of this method in the gear pump RUL prediction model are illustrated.
3. The KPCA method and a k-VNN-based JITL method are combined and used for the RUL prediction of gear pumps. The method proposed in this paper is compared to the traditional k-NN-based JITL method. The results show that the RUL prediction model proposed in this paper has a higher prediction accuracy for the RUL prediction of gear pumps. The method proposed in this paper comprehensively considers multiple characteristic parameters of gear pump pressure signals, which are of great significance for improving the RUL prediction accuracy of a gear pump.  
Conceptualization, [PERSON] and W.J.; Data curation, D.X.; Formal analysis, Z.L.; Methodology, Z.L. and W.J.; Software, [PERSON] and S.Z. ([PERSON]); Supervision, [PERSON] and S.Z. ([PERSON]); Visualization, [PERSON] and S.Z. ([PERSON]); Writing--original draft, [PERSON] and D.X.; Writing--review & editing, [PERSON], W.J., S.Z. ([PERSON]) and S.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 51875498), Key Project of Natural Science Foundation of Hebei Province, China (Grant No. E2018203339, F2020203058).  
Not applicable, this study does not involve humans or animals.  
Not applicable, this study does not involve humans.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.