Active magnetic bearings （AMBs） are ideal bearings for high speed and high power rotating machinery for its adjustable stiffness and damp. In this paper，a dynamic model of AMBs-flexible rotor system is established. Aiming at suppressing vibration displacement of the rotor system in passing through the first bending critical speed region，a control which combines a decentralized PID controller and input second filter in series is designed and the controller performances are simulated. The experiments in simulated rotation and real acceleration operations are carried out in a platform of AMBs-flexible rotor system. The rotor system can smoothly pass through its first bending critical speed region and the maximum rotor vibration displacement in acceleration operation is less than half of backup bearing gap. The rotor vibration displacement and current responses of the rotor in different unbalances are measured in order to analyses the influence of the rotor unbalance on vibration characteristics of AMBs-flexible rotor system. It is shown that the proposed controller can make the rotor system smoothly pass through its first bending critical speed region. The rotor imbalance has a significantly influence on the control performance and stability of AMBs-flexible rotor system. The experiment results give a support on the high-performance control strategy of AMBs-flexible rotor system.

As a core component of an aero-engine, the structural integrity of a blade directly determines the engine’s performance and flight safety. Under extreme working conditions such as high temperature, high pressure, and high-speed rotation, blades are prone to generating micro-cracks under the action of complex stress fields. Once cracks propagate and cause blade fracture, they will trigger chain damage, posing significant safety hazards. Based on the damage tolerance concept, the critical duration during which a blade can still operate safely after crack initiation is defined as the remaining useful life (RUL).To address this, this study proposes a mechanism-data dual-driven RUL prediction method integrating the Paris crack propagation law and physics-informed neural networks (PINN). By constructing a loss function that incorporates physical constraints, this method regularizes and constrains the gradients of the neural network. It enables inverse identification of crack propagation parameters while effectively improving the model’s prediction accuracy under limited monitoring data. For aero-engine blades and CT (compact tension) specimens, compared with traditional physical models and data-driven methods, the proposed method dynamically updates characteristic parameters to adapt to system changes, significantly reducing prediction errors under limited sample conditions. Additionally, the PINN model developed in this study features lightweight architecture and fast inference capabilities, meeting the requirements of online monitoring and predictive maintenance. This method provides a new technical pathway for health management and intelligent operation and maintenance of aero-engines.

Research on magnetically levitated rotors has been heavily influenced by studies on slender shaft magnetic levitated rotors. In the study on a certain magnetically levitated flat rotor for a centrifugal pump structure，both experiments and finite element analysis revealed that the support characteristics of the radial permanent magnetic bearings，with the same dual-ring structure，exhibited the significant differences from the known experience when applied to flat rotors. The translational stiffness and torsional stiffness showed substantial variations. This paper analyzes the variations in translational and torsional stiffness of permanent magnetic radial bearings for flat rotors based on changes in the bearing’s structural dimensions. Based on the analysis，a flat rotor magnetic levitation structure is proposed，which can increase and adjust the torsional stiffness of the permanent magnetic bearings，while also allowing for a rational ratio between translational and torsional stiffness. A finite element analysis is used to identify the structural conditions that yield maximum translational and rotational stiffness. The effectiveness of the proposed methodology is subsequently validated.

The camshaft is an important component that ensures the timely opening and closing of valve in marine diesel engines. Due to the poor contact lubrication state and excessive friction excitation，it is easy to cause larger interface torque on the camshaft. Considering the effects of transient excitation and interfacial friction，the tribo-dynamics model of the valve camshaft in V20 diesel engine is established. The forced vibration results of the camshaft are obtained，and the friction and lubrication performance of the cam-tappet pair is also analyzed under the fluctuating speed. The results show that，by thoroughly considering both the transient excitation and frictional excitation of each cam pair，the additional stress in each shaft section of the valve camshaft increases by about 4 MPa，while the transient speed fluctuation at the camshaft end increases by roughly ±30 r/min. Under the combined effects of speed fluctuation and surface roughness，the film thickness is dramatically reduced in some positions，especially in the cam base circle section where the film thickness decreases by about 0.3 μm. At the reverse motion position and nose of the cam，the temperature rise at the interface of the tappet exceeds the material’s scuffing temperature，thereby increasing the risk of scuffing wear.

Direct measurement of dynamic loads on engineering structures is challenging due to environmental constraints. Therefore，the indirect identification or reconstruction of dynamic loads，using dynamic response information，has emerged as a highly effective method. Over decades，dynamic load identification has evolved，resulting in a series of valid solutions. This paper begins by reviewing the research history and main achievements of dynamic load identification methods. It provides a systematic exposition of typical frequency domain and time domain methods，as well as dynamic load identification methods which are based on various approaches such as function fitting，regularization strategies，Bayesian frameworks，and data-driven techniques. The advantages and disadvantages ，as well as application scope of each method，are also discussed. Additionally，this paper summarizes common issues in the load identification process，including uncertainties in structural parameters and input conditions. Identifying the position of dynamic loads is also a crucial aspect of the dynamic load identification problem. This paper analyzes the methods currently available for position identification. This paper delves into the engineering applications of dynamic load identification methods and analyzes the limitations of current methods. Considering the current challenges in the field of dynamic load identification and the increasing demands in practical engineering applications，the paper anticipates the technical difficulties that need to be addressed. It also discusses potential future development directions and key areas in dynamic load identification.

The increasing complexity of intelligent equipment and evolving operation and maintenance demands within Industry 4.0 highlight the inadequate adaptability of traditional maintenance decision-making methods in dynamic environments. Reinforcement learning (RL)-based maintence decision-making technology offers a paradigm for intelligent equipment maintenance by enabling autonomous strategy optimization through environmental interaction. This paper systematically explores the integration of RL theory and maintenance decision-making, focuses on 76 peer-reviewed articles published between 1954 and 2024. Core RL algorithms, including SARSA, Q-Learning, and Actor-Critic, are thoroughly examined and analyzed. The current state of intelligent equipment maintenance decision-making technology is also analyzed in depth. Typical application scenarios for RL in equipment maintenance decision-making are comprehensively dissected across four key areas: industrial manufacturing, energy, aerospace, and transportation. The study also identifies and discusses the core challenges facing current technology, such as algorithm convergence speed, computational efficiency, model interpretability, and issues related to data acquisition and privacy. This research provides a theoretical reference for algorithm innovation and engineering implementation in the field of intelligent operation and maintenance, fostering the deeper application of RL in maintenance decision-making.

Mechanical metamaterials exhibit many counterintuitive mechanical properties by changing their internal geometry. We propose a mechanical metamaterial composed of gears as basic elements. The gear-based mechanical metamaterial proposed here is a multi-stable structure, which can be continuously converted between various stable states through the meshing of gear teeth. The continuous switching between states enables the mechanical metamaterial to exhibit in situ continuously tunable mechanical properties. The mechanical properties of the mechanical metamaterials were studied by using the finite element method. The results show that the gear-based mechanical metamaterials exhibited continuously adjustable stiffness, variable generalized shear stiffness, and adjustable acceleration transmissibility. These unique properties of mechanical metamaterials provide new ideas for creating programmable metamaterials with in situ continuously adjustable mechanical properties, and are expected to be applied in the fields of smart materials and engineering.

Current research on fault diagnosis for aircraft complex motion mechanisms primarily focuses on system functional failure analysis, neglecting a comprehensive understanding of the correlation between motion characteristics and actual faults. This study investigates fault diagnosis methods for complex motion mechanisms and proposes a three-tiered framework encompassing data generation, feature processing and data analysis to address this limitation. The framework utilizes dynamic modeling and a fault parameter system to generate a dataset of time-series signals representing typical fault conditions. One-dimensional time-series data are mapped using two-dimensional image conversion methods, constructing multidimensional tensors through feature-level fusion based on sensor types and feature extraction methods of the complex motion mechanisms. A deep learning-based fault diagnosis model is employed for precise fault identification of complex motion mechanisms. This framework further incorporates collaborative feature transformations using Gramian angular fields and Markov transition fields, as well as residual network models with channel and spatial attention mechanisms. Experimental validation using a landing gear lower strut lock mechanism demonstrates high accuracy, exceeding 0.9566 at a 95% confidence level, thus validating the feasibility of this approach for fault diagnosis in aircraft complex motion mechanisms. Ablation experiments confirm the effectiveness of each component, highlighting the overall superiority of the proposed framework.

Key components of industrial robots are prone to early-stage performance degradation under complex operating conditions, characterized by strongly non-stationary responses and significant heterogeneity across sensing channels. Traditional diagnostic methods struggle with robust and interpretable fusion of multi-source information, limiting their practical deployment. This paper proposes a dual-channel intelligent diagnostic method for robotic transmission mechanisms, integrating physics-driven sensitivity weighting and residual uncertainty compensation (RUC). Specifically, vibration and torque signals, representing structural response and driving excitation respectively, are selected due to their distinct temporal scales and complementary physical characteristics. A three-layer mapping (fault type-dynamic response characteristic-sensing channel) is constructed to quantify channel dominance for different fault modes. Then, a multi-scale sensitivity evaluation mechanism based on signal-to-noise ratio (SNR), modulation index (MI), and kurtosis guides adaptive weight allocation, while the RUC strategy enhances the expression of features from weakly dominant channels, improving fusion stability. Finally, a physically interpretable and lightweight diagnostic framework is established. Experiments conducted on a public gearbox dataset validate that the proposed method provides superior diagnostic accuracy, interpretability, and deployment potential, demonstrating significant promise for physically consistent multi-source fusion diagnosis in robotic transmission systems.

The existing shell model is accurate in the frequency band of 0~500 Hz，making it highly suitable for tire vibration analysis. However，the modal properties of the tire belt cannot be obtained separately and the freedom as well as the number of parameters required by the model increases，because the shell model couples the tire belt and sidewall. Therefore，this shell model was improved in this paper. The shell model for simulating the tire belt remained unchanged，whereas，by using the method for approximating the sidewall used in the ring model and plate model of tire，the two-dimensional shell model for simulating the tire sidewall was replaced by an elastic foundation to simplify the boundary conditions of the tire belt and reduce the number of parameters related to the tire sidewall. The solving method for the improved shell model was developed to calculate the modal frequency and the modal shape of the tire belt. The validity of both the improved shell model and its solving method was demonstrated by an experiment.