Severe oil particle-induced erosion to the prestage component progressively degrades the overall performance of the deflector jet servo valve (DJSV), even leading to valve failure. Herein, we present an approach for evaluating degradation in performance and predicting the erosion lifespan of the DJSV on different levels of oil pollution. Specifically, a mathematical model of the whole valve was built based on a previously established working principle and physical mechanism. In addition, considering the horizontal and rotational particle motions, combined with impact of particle size distributions under different oil contamination degrees, an erosion model was constructed. Then, after simulating and analyzing the pressure characteristics before and after the erosion of prestage, the performance degradation of the whole valve was examined, thereby predicting the erosion life of the valve. Investigations revealed that the maximum erosion rate occurred at the shunt wedge of the receiving holes, which increased with the contamination degree and accelerated after level 7. After erosion, however, the control pressure difference decreased significantly, and erosion life followed exponential distribution corresponding to the distribution of particles under different pollution levels. The aforementioned investigation can thus help diagnose faults and optimize the design of the servo valves in service.

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.

Fault diagnosis plays a critical role in industrial equipment health monitoring, while effective multichannel sensor data processing is a key to efficient and accurate fault diagnosis. This paper proposes a novel multichannel signal adaptive fusion framework, namely MCFPred, for fault diagnosis in hydraulic systems. Specifically, MCFPred adopts a data acquisition module to capture multidimensional equipment operational signals via multichannel sensors first, and then, employs a Adaptive Weighted Non-symmetric Projection for Time series Fusion (AWNPTF) module to adaptively fuse multichannel time series data to generate high-quality two-dimensional (2D) feature maps. Finally, a deep residual network (AIRNet) is devised and utilized for feature extraction and classification. In MCFPred, AWNPTF helps realize adaptive weighted multichannel time series data fusion with improved computational efficiency, while AIRNet incorporates Channel Attention Mechanism (CAM) and Spatial Attention Mechanism (SAM) to improve fault feature extraction and diagnostic accuracy. Extensive experiments with publicly available hydraulic system datasets are conducted to verify the effectiveness of MCFPred. Results convincingly demonstrate that MCFPred outperforms those existing methods in different cases with an average fault diagnosis accuracy over 99% been achieved. Both stability and generalization of MCFPred are further validated with another bearing fault dataset.

The deflector jet pressure servo valve (DJPSV), a critical component of the aircraft brake servo system, requires a precise foundational model for performance analysis, optimization, and enhancement. However, the complexity of the jet process within the V-groove of the deflector plate presents challenges for accurate mathematical modeling. To address this issue, the paper takes the DJPSV as the research object, carries out detailed mathematical modeling of its components, analyzes the influencing factors of the performance of the key component—the front stage—and optimizes the design of the key factors. First, integrating FLUENT velocity field analysis, this study proposes a novel perspective to rationally simplify and parametrically model the injection process in 3D space. Subsequently, a systematic deduction of the mathematical model for DJPSV is undertaken. Employing the AMESim platform and the secondary development module AMESet, a comprehensive simulation model is constructed, facilitating the study of static-dynamic valve characteristics. Additionally, utilizing the Morris theory and an intelligent algorithm, sensitivity analysis, and structural optimization on the critical component, the pre-stage. The results reveal that the width of the receiving diverter wedge (M), the width of the V-groove outlet (b1), and the distance from the V-groove outlet to the receiving diverter wedge (h) exert the most significant influence on the differential pressure of the pre-stage, which are the key parameters affecting the output differential pressure of the pre-stage. The experiment verifies the accuracy of the simulation model, offering a vital theoretical foundation for valve development and related areas.

Space robots must be designed with a large operating radius and lightweight components (flexible joints and flexible links). The integration of flexible components inevitably affects the control accuracy of the robot. When modeling the link deformation of the space robot, the idealized description method has the problem of incomplete accuracy in link deformation due to the coupling effects of the non-fixed base, flexible joints, and changes in control parameters. To address this challenge and improve the accuracy of robot modeling, this study proposes a dynamic coupling analysis method for the link deformation of space robots based on time-domain response. The double finite difference method is adopted to calculate the time-domain response of link deformation. On this basis, using the local linear operator method, the modal information of the deformed links is extracted. This method systematically quantifies the individual and interactive effects of the robot’s base motion, joint flexibility, and control parameters on link deformation. This work not only lays a theoretical foundation for the accurate dynamic modeling of space robots but also provides practical insights for optimizing their structural design. Ultimately, it contributes to the development of more reliable and high-performance space robot systems.