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.

Hydraulic systems are complex nonlinear systems with concealed fault characteristics, making accurate fault diagnosis challenging. To address these issues, this paper develops a lightweight multisensor fusion framework, DASA-Fuse, for hydraulic components fault diagnosis. The framework consists of two main components. First, a novel time-series representation method, Density-Aware Partitioned Recurrence Plot (DAPRP), is proposed. By introducing a density-aware symbolic modeling mechanism, DAPRP reformulates conventional recurrence analysis, enabling robust characterization of nonlinear dynamic behaviors under noise interference and non-uniform data distributions. Based on DAPRP, a Multichannel DAPRP (MDAPRP) is further constructed to achieve unified fusion of heterogeneous multisource sensor signals. Second, a task-specific attention mechanism, Grouped Spatial Cross-Attention (GSCA), is proposed to model directional structural dependencies in recurrence-based representations. GSCA is further embedded into the MobileViT architecture to construct a lightweight network namely SAMViT, which enhances the extraction of fused structural features without significantly increasing computational overhead. Experimental results on both a self-made hydraulic test rig dataset and a public dataset demonstrate that DASA-Fuse achieves average diagnostic accuracies of 96.69% and 97.46%, respectively. Furthermore, compared with existing state-of-the-art methods, the proposed approach exhibits favorable diagnostic performance and cross-dataset applicability.

To address vibration issues in the operation of a lift-type power catwalk, dynamic analysis was conducted using ADAMS 2020 software. The analysis revealed that the vibrations originate from collisions that occur during the transition from the translation to the climbing phase. A quantitative vibration model was developed using the work–energy theorem, incorporating the coefficient of restitution. Based on this model, a composite disturbance rejection control law was designed, integrating sliding-mode speed planning with active disturbance rejection speed tracking. The MATLAB 2022b/Simulink simulations demonstrated that the ADRC strategy outperforms PID and DDPG control algorithms in terms of robustness. Co-simulation with ADAMS and MATLAB/Simulink confirmed the effectiveness of sliding mode speed planning, resulting in a 13.54% increase in the transport efficiency of lift-type power catwalks for drill pipes. Additionally, the oscillation of the output speed of the electro-hydraulic servo system at the collision point was reduced by 300% and the displacement, velocity, and acceleration fluctuations in the catwalk’s mechanical system were reduced by 85%, 75%, and 90%, respectively. The experiments on a lift-type power catwalk prototype confirmed that the proposed vibration suppression strategy effectively reduces vibrations.

Controlling and maintaining the orientation of the balloon-borne gondola for high-altitude flight is a prerequisite for ensuring the pointing control of observation instruments. When the balloon-borne gondola is flying in the stratosphere of the atmosphere, the existing external interferences will be converted into the coupling moment to the azimuth control system. Meanwhile, those uncertain factors and the frictional nonlinearity of the control system will also cause a certain magnitude of coupling moment. The existence of such coupling moment largely impacts on the accuracy and stability of the orientation control for the angular momentum exchange devices of the balloon-borne gondola. To address such an issue, this paper proposes and implements a novel type of integrated decoupler device. With this decoupler adopted, the aziDmuth control system could sense the existence of coupling torque and azimuth fluctuations quickly and suppress the influences of external interference, uncertain factors, and system structure nonlinearity on the azimuth control effectively, thereby improving the control accuracy of the azimuth control system. Both simulations and experiments are conducted to verify the effectiveness of the proposed device. The results show that the integration of the decoupler and the controller of the azimuth control system provide the azimuth control of the balloon-borne gondola with high accuracy and stability. Such a decoupler device design has a broad potential and could not only be used for balloon-borne gondola control but also could be applied onto other control systems using angular momentum exchange devices as actuators.