As the main actuator in hydraulic systems, motors generate significant noise radiation that increasingly fails to meet low-noise requirements. Due to unresolved issues such as unclear primary noise sources and low localization accuracy, noise reduction in motors remains challenging. Therefore, to identify the main noise sources and improve localization accuracy, this study employed multiple approaches: A fluid simulation model of the motor was established using Pumplinx software to obtain the variation of fluid excitation forces at the motor’s port plate. A cosimulation using ADAMS and AMESim was conducted to acquire the variation of excitation forces caused by pistons impacting the cylinder block during motor operation. Combined with transient finite element analysis, the transient analysis method in ANSYS was used to obtain the vibration displacement response on the surfaces of the motor housing and rear end cover. Using this vibration data from ANSYS as acoustic boundary conditions, a boundary element analysis was performed in LMS Virtual Lab to simulate the motor’s acoustic field, thereby identifying the main noise sources and primary noise generation areas. Subsequently, a motor sound intensity noise test bench was designed to obtain sound intensity variation cloud maps, verifying the accuracy of the multi-physics simulation results. Then, considering the relationships among the observation matrix, sparse representation, and reconstruction algorithm, the regularized orthogonal matching pursuit reconstruction algorithm was adopted to determine the localization areas of the main motor noise. Finally, the feasibility of the optimized reconstruction algorithm in improving localization accuracy was verified with the sound intensity test bench. The results show that the multi-physics field simulation of the motor model is correct, the main noise sources are the pressure impact at the valve plate and piston collisions, the main noise area is distributed around the valve plate, and the new localization accuracy reaches 25 mm, achieving the determination of the main motor noise sources and an improvement in localization accuracy.

The internal noise sources of the bent-axis piston motor are close in distance. For example, the distance between the inlet and outlet of the motor valve plate is 38 mm, and the noise sources have the same frequency and multiple frequency phenomenon. The dense and complex noise sources in the bent-axis motor cause difficulties for the spectrum analysis method to accurately identify the same frequency and multiple frequency signals. The maximum resolution of traditional sound intensity measurement is 50 mm, which cannot meet the requirement of identification accuracy of motor internal noise source. Aiming at the problem that the traditional methods are difficult to identify the motor noise sources accurately, this paper proposed a sound intensity measurement method based on compressed sensing. The compressed sensing theory was applied to the high-precision reconstruction of sound intensity image to obtain the high-resolution sound intensity reconstruction image of the motor. Firstly, the noise radiation simulation of the bent-axis motor was carried out to obtain the characteristics of its external surface sound field. Then, based on the sound intensity image for the motor, a compressed sensing frame applied to the motor sound field was designed to obtain the sound intensity cloud image of the motor with high precision. Finally, the feasibility of the compressed sensing theory to improve the identification accuracy of motor noise source was verified by comparing the traditional acoustic intensity measurement with the compressed sensing acoustic intensity measurement. The results show that the identification scale of motor noise sources are improved from 70 mm to 30 mm by the sound intensity measurement method based on compressed sensing, which improves the accuracy of motor noise sources identification and realizes the high precision location of motor noise sources.

High-speed on/off valves (HSVs) are widely used in the aerospace industry. The large eddy currents make it difficult to improve their response time, and the relationship between response time and various structural parameters such as armature diameter and spring preload is complex. To address this issue, the influence of eddy currents on the dynamic characteristics of HSVs is analyzed. Based on this, a multi-slit armature structure is designed to reduce eddy current loss, which can accelerate the opening and closing of the electro-mechanical transducer in HSVs. The effects of the interaction between armature diameter, coil turns, spring preload, and spring stiffness on the opening and closing characteristics of the electro-mechanical transducer are then analyzed. Subsequently, the correlation between each factor and the opening and closing times is quantified using grey correlation analysis. The results show that the multi-slit armature structure can reduce eddy current loss by 50.07% and shorten the opening and closing time by 15%. Moreover, it is found that spring preload has the highest correlation with closing time, while armature diameter is most correlated with reset time. These findings can serve as a basis for optimizing the structure of the electro-mechanical transducer and further improving the dynamic characteristics of HSVs.