## 1 Introduction
[14] used computational fluid dynamics (CFD) and combined turbulence theory and microerosion principles to develop a physical model of the erosion suffered by a novel linear electro-hydrostatic actuator (LEHA) under high-frequency turbulent flow conditions, and explored the effect of the concentration of contaminated solids on erosion. Similarly, scholars [PERSON] et al. [15] established a mathematical model of erosion wear for a conventional electro-hydraulic servo valve, using its structural wear combined with CFD theory, and discussed the effect of each structure of this electro-hydraulic servo valve on erosion wear and servo valve performance. However, the above methods are too dependent on the material properties of the components and the influence of the forces to which they are subjected and are analyzed purely from a physical or mechanical point of view. Such influences are not fully controllable in practical industrial applications, and the structural requirements for servo valves are limited and less universal, and therefore cannot be fully and universally applied in industry. With the rise of the information age, there has been considerable development in intelligent data-driven processes and big data, and in particular an increase in the research and application of neural networks as a core strategy for machine learning and artificial intelligence, for example, which has led to an increase in the accuracy and precision of prediction results in terms of lifespan prediction. Scholars [PERSON] et al. [16; 17] used a combination of non-dominated sorting genetic algorithms and first-order shear deformation theory to model the frequency-based acoustic properties of an elastic material core and its shell as a means of determining the loss of the object of study. [PERSON] et al. [18] of Beijing University of Aeronautics and Astronautics (BUAA) established fault and health observations, as well as performance degradation predictions for hydraulic servo systems based on Elman network observer and support vector regression (SVR). However, this algorithmic model is suitable for highly non-linear and non-stationary conditions and is less effective in the analysis and forecasting of time-series seasonal and even non-seasonal regularity data. In this research project, support vector machine algorithms have been used to predict the life of direct-drive electro-hydraulic servo valves, but the results are less effective than other algorithmic models, and the errors are greater. Others, such as [PERSON] [19], designed a system for monitoring and testing the service life of hydraulic shock absorbers in vehicles using various sensors, data acquisition cards, and Labview as a PC. However, such algorithms are good for fault diagnosis and monitoring, but how effective they are for predicting future trends is open to question. [PERSON] et al. [20] from the University of Cincinnati, USA, proposed a migration learning method for lifespan prediction using consistency-based regularisation, which avoids the effect of missing data and finally validates its effectiveness experimentally. [PERSON] et al. [21] from Harbin Engineering University proposed a method that can be applied to the life prediction of electric valves in nuclear power systems by combining the advantages of convolutional encoders and long- and short-term memory neural networks in terms of depth and time breadth, respectively. This method allows for a parallel structure with neural network characteristics to be built between the input and output of the initial data. [PERSON] et al. [22] used a deep-neural-network-based optimization model to predict the battery life of an IoT system and then implemented the moth flame optimization (MFO) algorithm to select the optimal data features before feeding this feature into the neural network model for life prediction. [PERSON] et al. [23] also specifically address the relationship between lifespan prediction and neural networks and machine learning.