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Owing to its unique structure, the driving motor of a ship’s rim propulsion device is subject to coupling effects from multiple physical fields. This makes it difficult for conventional control methods to effectively suppress vibrations caused by high-amplitude, complex harmonics, leading to poor speed and torque control performance. Therefore, a vibration suppression method based on disturbance observer combined with non singular sliding mode control is proposed. First, a disturbance observer is constructed to monitor motor torque in real time and accurately capture torque fluctuations induced by vibration. Secondly, design a non singular sliding mode controller to adaptively and quickly adjust the motor speed when vibration is detected. Finally, the quantum particle swarm algorithm, enhanced by the artificial bee colony algorithm, is used to optimize the controller parameters, thereby improving robustness and accuracy under multi-physics field coupling. The experimental results show that this method can accurately observe the motor torque and quickly stabilize the speed between 500 r/min-800 r/min under vibration state, with the smallest torque fluctuation amplitude. This result holds important scientific significance: it validates the effectiveness of nonsingular sliding mode control combined with intelligent optimization algorithms in decoupling multi-physics field interactions and suppressing complex electromagnetic excitation vibrations, offering a new control perspective for understanding motor dynamics under extreme operating conditions. In terms of application value, this method significantly enhances the dynamic response speed and steady-state accuracy of the driving motor, directly improving propulsion efficiency and maneuverability. It also effectively reduces fatigue wear on mechanical components, extends equipment life, and lowers operation and maintenance costs throughout the ship’s life cycle. In the future, we will explore integrating this control strategy with energy efficiency optimization for propulsion devices and investigate predictive vibration suppression methods based on digital twins to achieve smarter, more efficient health management of ship propulsion systems.

. In order to attain time-lag dynamic compensation control and decrease time-lag influence of high-clearance sprayer semi-active suspensions, a novel suspension intelligent controller based on improved Smith prediction neuroendocrine algorithm is proposed. Firstly, the time-lag semi-active suspension dynamic model of high-clearance sprayers is established, the neuroendocrine intelligent controller is designed combined with the hormone regulation mechanism in the organism. Then, by combining enhanced Smith prediction compensation controller with neuroendocrine intelligent controller, a kind of high-clearance sprayer semi-active suspension intelligent controller based on the improved Smith prediction neuroendocrine algorithm is developed. The research results show the proposed algorithm can significantly reduce body vertical acceleration (BVA) and tire dynamic load (TDL) indicators of high-clearance sprayer semi-active suspensions, effectively improve smoothness and road friendliness of sprayers, significantly enhance comprehensive performance, show strong adaptability and robustness under working conditions, and is very suitable for vibration control of the high-clearance sprayer semi-active suspension with high order time-varying, complex nonlinear and strong coupling.

Compressors serve as fundamental power units in underground natural gas storage facilities and frequently need to accommodate wide-range operational condition adjustments. However, their inherent nonlinear characteristics, coupled with high-noise and uncertain operating environments, pose significant challenges to precise control and safe operation. To accurately predict compressor operational status and provide reliable decision-making information for operation and maintenance, this study proposes an operating-state prediction method for gas storage compressors based on vibration-sequence analysis and a Bidirectional Long Short-Term Memory (BiLSTM) network. Singular Spectrum Analysis (SSA) is first applied to decompose and reconstruct monitoring signals, extracting dominant trend components while suppressing background noise. A Convolutional Neural Network (CNN) is subsequently employed to capture multi-scale local features, after which the BiLSTM network learns temporal evolution patterns from the extracted features. The Adam optimizer is utilized to enhance training stability and prediction accuracy. Experimental validation using real monitoring data from gas storage compressors indicates that the proposed method achieves precise and reliable prediction of compressor operating states.

As a localized high-intensity downdraft disaster, downbursts are a significant cause of wind-rain-induced damage to transmission lines. Their unique wind field characteristics make it difficult for existing design methods to comprehensively evaluate the resistance capacity of transmission lines. Current collapse analyses of transmission lines often fail to adequately consider the wind-rain field conditions during downbursts. Therefore, this study investigates the dynamic response and collapse mechanisms of transmission lines under downburst wind-rain conditions. First, a numerical simulation model is established to explore the distribution characteristics of wind and rain. A full-scale three-dimensional computational domain model is employed to simulate the wind-rain field, which is subsequently modified. The wind-rain velocity ratio is analyzed, and a fitting formula is proposed. Subsequently, combined distributed loads are applied to the transmission line to conduct parametric analyses of the dynamic response and investigate the collapse mechanisms. The results demonstrate that the computational domain model for simulating the wind-rain field is validated using relevant models, and the Vicroy model is modified for generating wind-rain loads. The horizontal velocity of raindrops does not synchronize with wind speed variations, and the proposed fitting formula for the wind-rain velocity ratio exhibits high accuracy. Downbursts significantly influence the dynamic response of the transmission tower-line system, with the most unfavorable wind attack angles, heights, rainfall intensities, and combined conditions identified. The collapse failure mode of the transmission line is characterized by initial damage and failure of diagonal members, leading to extensive structural collapse. The critical segments vary under different wind attack angles and reference heights, while rainfall has a minor impact on the collapse process. This study provides important technical insights for the wind-resistant design and safe operation of transmission lines.

In response to the lack of clear calculation methods for the normal section bearing capacity of circular piers strengthened by concrete jacketing in current design codes, this paper derives a calculation formula applicable to the normal section bearing capacity of strengthened circular piers under unloading conditions. The derivation is based on the relevant provisions of the Specifications for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts, incorporating the plane-section assumption and ultimate state theory. Finite element verification shows that the theoretical values agree well with the simulation results, with a maximum error of –1.55 %, and the results are conservative, meeting engineering safety requirements. In terms of impact resistance, increasing the thickness of the strengthening layer significantly enhances the pier's stiffness, reduces the displacement peak, and shortens the dynamic response time, shifting the damage mode from global damage to locally controllable damage. Parameter analysis indicates that the diameter of the main reinforcement and the spacing of the stirrups have a limited effect on the displacement response but play a key role in energy dissipation capacity: increasing the main reinforcement diameter effectively improves the total energy dissipation, while reducing the stirrup spacing enhances the confinement effect on the core concrete and improves energy dissipation efficiency. Considering both economy and construction feasibility, it is recommended to prioritize larger diameter main reinforcement and control the stirrup spacing within the range of 10-15 cm in impact-resistant design to achieve an optimal balance between performance and cost. Combined with a bridge strengthening project in Changchun, this paper summarizes key technical points, forming a theoretically complete and practically verified technical system for pier strengthening.

To address the challenges of fault feature extraction and the weak adaptability of diagnostic models for automotive gearbox bearings under complex operating conditions, this study proposes an improved intelligent diagnostic model that integrates Convolutional Neural Networks (CNN) and Transformers with a dual-stage dynamic sparse activation and three-dimensional attention mechanism. First, to overcome the limitations of traditional CNN with fixed architectures and limited perception of multi-domain fault features, a dual-stage dynamic sparse activation mechanism is designed. It enables adaptive computation path selection based on the complexity of input features. Then, to enhance the perception of multidimensional time-frequency-phase fault information, the Hilbert transform is applied to construct a three-dimensional feature tensor containing instantaneous amplitude, frequency, and phase. A 3D self-attention module is embedded to achieve multi-domain feature fusion. Finally, the proposed method is validated using experimental data collected under various gearbox bearing fault states and operating conditions. The results show that the model achieves an accuracy of 99.73 %, with precision, recall, and F1-score of 99.64 %, 99.63 %, and 99.68 %, respectively – all outperforming state-of-the-art methods such as GDS-YOLOv5s. Moreover, the model maintains stable recognition performance under noise and variable load conditions. These findings demonstrate that the proposed approach effectively captures subtle multi-domain fault features and exhibits strong adaptability and robustness, providing a reliable solution for intelligent operation and maintenance of gearbox bearings.

This study focuses on a double-tower, double-plane prestressed concrete cable-stayed bridge with a span arrangement of 75+130+365+130+75 m. A full-bridge finite element model was developed using Midas Civil, and a monitoring system was established to track main girder alignment, cable forces, and structural stresses. A phased control and monitoring strategy is implemented: alignment control is prioritized during the cantilever casting stage, while cable force and stress control take precedence during the closure stage. Monitoring results indicate that the alignment of the completed bridge's main girder is smooth, with cable force deviations controlled within 10 %, and stresses in both the main girder and pylons remaining within safe limits. This study integrates dynamic control and monitoring strategies, offering practical references for the construction control of similar bridges.

To address the issues of insufficient low-frequency damping and high-frequency stiffening in magnetorheological mount systems, this paper proposes a novel mount structure combining a bell plate with a squeeze model. Through establishing a lumped parameter model, conducting magnetic field simulations using Ansys, and integrating dynamic characteristic analysis, the study concludes that: 1) The bell plate structure significantly reduces high-frequency dynamic stiffness by 35.89 %, effectively suppressing high-frequency stiffening; 2) The squeeze channel markedly enhances low-frequency dynamic stiffness and loss angle, thereby resolving the issues of insufficient low-frequency damping and stiffness.