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Axial piston pumps are frequently used in hydraulic applications which require high pressures. In the axial piston pump shown in Figure 1, one of the most important locations for metal–metal contact is between the slipper and the swash plate.

Although analytical and numerical solutions enhance our understanding of the physical phenomena occurring between the slipper and swash plate, they are based on certain assumptions. When the inertial effects of solids and fluids, thermal factors, and vibration are considered, mathematical solutions become more difficult. In such cases, machine learning methods (MLMs) offer the ability to model non-linear systems without any assumptions. The use of a neural network (NN) for axial piston pumps was first realized by Kakaub et al. [9]. In their study, the data obtained from an experimental setup was used to reduce the power loss under high pressures. They demonstrated that an NN trained using Levenberg–Marquardt optimization was able to predict the steady-state and dynamic behavior of the pump accurately. In another study, an NN was employed to predict the pressure distribution and load-bearing capacity [10]. The results demonstrated that NNs are more effective than theoretical models for determining the pressure distribution and load-bearing capacity. Canbulut et al. [11] reported an experimental study of the load bearing capacity, leakage, friction moment, and pump power properties of the slipper under different working conditions, and developed an NN to predict the results. This NN exhibited superior performance at predicting the static and dynamic parameters of the bearing system. In another study by Canbulut et al. [12], the effects of the surface roughness of the slippers on lubrication were studied experimentally. They also observed that surface roughness has important effects on leakage. In addition, they designed the behavior of this slipper using an NN, and argued that their model could be used in real-time applications. Another study investigated the power dissipation of conical and flat-surfaced slippers and observed that the slippers with flat surfaces performed better. Moreover, the power dissipation was modeled using an NN, which was found to adapt very well to experimental data [13]. Canbulut et al. [14] also studied the frictional power loss of the slipper theoretically and experimentally. They observed that the study parameters and surface geometry contribute to the frictional power loss, and formed an NN model for frictional power loss with high predictive capabilities. Ozmen et al. [15] modeled the pressure distribution and leakage of a flat slipper working hydrostatic/hydrodynamically using Multi-Gene Genetic Programming (MGGP) and NN, MLMs. The results showed that both MLMs had high predictive performance in terms of pressure distributions and leakage for a flat slipper. However, analytical equations showed lower performance than MLMs because of neglected of flow inertia and system noise. Following an experimental analysis of the hydrodynamic and general efficiency variations of gear pumps, vane pumps, and axial pumps, an NN prediction model was developed to act as a predictor in the applications of such systems [16].

The objective of this study is to experimentally research the leakage, oil film thickness, and pocket pressure of different slipper surface geometries under various working conditions. Although many NNs have been used to predict the complex relationship between the slipper and swash plate, they typically require a high number of external parameters to be entered, their performance is directly dependent on the optimization method, and they are extremely susceptible to noisy data. To overcome these disadvantages, we investigate the application of a long short-term memory (LSTM)-based deep neural network (DNN), which offers superior performance in predicting time series. Compared with conventional MLMs, DNNs can achieve superior classification and modeling performance [17]. To the best of our knowledge, DNNs have not yet been used for this purpose. The results derived in this study have been compared with those from conventional MLMs such as linear regression (LR), decision tree (DT), support vector machine (SVM), and NN. The proposed model is statistically proven to be superior to all of these methods.

This article is structured as follows. In Section 2, the working principle, experimental setup, and experimental method of axial piston pumps are briefly summarized. Next, in Section 3, the DNN prediction model is explained. Section 4 presents and discusses the experimental results, and Section 5 gives our conclusions and ideas for future work.