# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 1 Introduction  
As the power source, the piston pump affects the function realization of the whole hydraulic system. According to the performance degradation of the piston pump so far, the degradation state recognition is carried out to determine the performance of the piston pump and the whole hydraulic system [1], which can provide decision-making information for condition-based maintenance (CBM), prognostics, and health management (PHM) [2]. Due to the influence of oil, temperature, load, and other factors [3; 4], the components of piston pump will deteriorate because of wear [5], and this degradation will eventually lead to changes in the main performance indicators of the hydraulic pump. The sealing belt on the bottom surface of the slipper will deform under the working condition, which will cause the inner edge to bulge. At the same time, the oil film thickness between the slipper and the swash plate will become thinner [6]. If there are pollution particles in the hydraulic oil, the inner edge of the sealing belt is easy to be worn [7]. Therefore, in actual production, the inner edge of the slipper sealing belt is most seriously worn, which will directly lead to the performance degradation of the pump, as shown in Figure 1. Therefore, the wear of the inner edge of slipper should be considered as the main degradation form when evaluating the degradation state of piston pump. According to the different wear states of slipper, the vibration signals of piston pump shell are different in multi-directional and multi-dimensional features, so how to extract effective degradation index from them is the key to state recognition. The vibration signal is usually transient and periodic pulse behavior, which contains critical information about the status of the mechanical equipment. Scholars have carried out a lot of research and analysis of the vibration signal of mechanical equipment to extract effective information. [PERSON] et al. [8] used and improved the multi-fractal detrended fluctuation analysis (MF-DFA) to extract the performance degradation characteristics of the hydraulic pump vibration signal, which improved the accuracy of the degradation state recognition, but did not process the vibration signal noise of the pump. [PERSON] et al. [9] used wavelet packet transform to analyze the bearing vibration signal, and extracted the node energy and its total energy as features. The method has a certain effect on white noise suppression, but the ability to suppress signal pulse interference is not strong. [PERSON] et al. [10] applies pseudo-fault signal (PFS) assisted empirical mode decomposition (EMD) on the envelope, which solves the problem that different fault frequencies are not obvious due to mode aliasing, but the decomposition efficiency of the algorithm is low. [PERSON] et al. [11] proposed an improved Hilbert-Huang transform based on ensemble empirical mode decomposition (EEMD) and sensitive mode IMFs, and achieved good results to a certain extent. For EEMD [12; 13], the method reduces the effect of mode aliasing by adding auxiliary white noise; however, different implementation of signal plus noise may produce a different number of modes, making the final averaging difficult [14]. To solve this problem, [PERSON] et al. [15] proposed complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), which added adaptive white noise at each stage of decomposition, and then obtained the modal components of each layer by calculating the unique residual signal. For the degradation state recognition, [PERSON] et al. [16] judged the degradation state of hydraulic pump by calculating the Jensen-Renyi Divergence (JRD) distance between different characteristic variables. For the fuzziness of the boundary of different degradation states, [PERSON] et al. [17] used the fuzzy c-means (FCM) method to cluster and identified the degradation state of bearing performance according to the maximum membership degree law, the overall recognition rate reached 96%. [PERSON] et al.