In the nozzle flapper servo valve, the transient flow force on the flapper is the fundamental reason that affects the pressure stability. The pressure pulsation in the pilot stage causes forced vibration of the flapper, and its deviation will directly influence the control pressure difference, which will make the pressure appear unstable. In order to grasp the principle and characteristics of transient flow force and its influence on pressure stability, a mathematical model of flapper displacement and control pressure is derived. For collecting the dynamic changes of the transient flow force and recording the motion behavior of the flapper, a three-dimensional model of the pilot-stage is established. Numerical simulations of turbulence phenomenon analysis are conducted with a variation of flapper displacement ranging from 5 $\mathsf{\mu }$m to 20 $\mathsf{\mu }$m. It can be concluded that the change trend of the flapper displacement is similar to the steady-state flow force and the transient flow force pulsation amplitude. Under the same structural parameters, the pulsating frequency of the flow force remains basically constant. The fluctuation of the flow force of the pilot stage will cause the pressure of the servo valve control cavity to vibrate to a certain extent, which is a factor that cannot be ignored that affects the output stability of the servo valve.

Hydraulic systems play a central role in the transmission and control of various industrial equipment, and the consequences of failures can be severe. The high pressure and closed nature of hydraulic systems, coupled with poor measurability of parameters, limit the applicability of many fault diagnosis methods. In order to achieve automatic fusion of multi-sensor data and accurate fault diagnosis, this paper proposes a novel intelligent fault diagnosis model (IFDM) that combines Variational Mode Decomposition (VMD) with residual networks incorporating attention mechanisms. Utilizing adaptive VMD along with multiscale dispersion entropy allows for the automatic extraction of features from multi-sensor data without the need for specialized knowledge, making it more suitable for industrial applications. The residual networks with attention mechanisms allocate weights to each channel, enabling the model to better learn the relationships between channels and enhance its focus on different fault features. Experimental results demonstrate that the proposed method can accurately diagnose various levels of faults in multiple components of hydraulic systems, achieving a classification accuracy exceeding 99% with superior generalization capabilities.

In this study, we comprehensively investigate the structure and operational principles of the Rotary Direct Drive Electro-Hydraulic Pressure Servo Valve (RDDPV). Our objective is to establish the dynamics equations governing the motor, slide valve, and bias mechanism of the valve. Additionally, we construct a mathematical model for the servo valve controller, while ensuring the linearization of the controller model. Furthermore, we conduct an in-depth analysis of the static characteristics of the valve, including linearity, dead zone, hysteresis loop, and zero drift. Regarding the dynamic characteristics, we establish a dynamic mathematical model for the RDDPV valve. Subsequently, we subject the servo valve to analysis with a focus on frequency response and dynamic response, using the control current as the input and the pressure as the output. To perform these analyses, we employ the software package SIMULINK of MATLAB, facilitating dynamic simulations. Remarkably, the simulation results exhibit the valve's conformity to design requirements, underscoring its suitability for subsequent research and development endeavors. Through our rigorous investigation, we offer essential technical support for the forthcoming stages of the valve's research and development, thereby laying a robust foundation for its further advancement.

Operating point drift over large temperature spans can significantly degrade the performance of servo valves. The direction and magnitude of the deviation