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.

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.

Bearing fault diagnosis is an important research topic in aviation engine prediction and health management. Signal processing algorithms and deep learning models in this field rely on datasets. However, publicly available datasets generally cover narrow speed ranges, large speed intervals, single loads, and a lack of composite fault data, making it difficult to support the practical development of fault diagnosis methods. This article discloses a vibration dataset of aircraft main shaft bearings with a wide speed range. In addition to providing single fault data, this dataset also provides multiple composite bearing fault data, covering multi-channel bearing vibration signals with a wide speed range under different loads. The dataset well supports the research of classic fault diagnosis algorithms, and due to the large speed range covered by the data and high-speed sampling rate, it is more conducive to training deep learning fault diagnosis models.

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.

Based on a large depot project in Chongqing，the vibration and secondary noise characteristics and human comfort of the over-track buildings induced by metro operation in the depot are studied. The vibration characteristics of the depot are analyzed by field measurement. Combined with the numerical simulation method，the finite element model of the track-soil-depot-over-track buildings is established based on the metro-track coupling theory and the finite element theory. The vibration and secondary noise characteristics of the top buildings under train excitation are analyzed and evaluated. The combined annoyance model of vibration and secondary noise is constructed by combining psychology and fuzzy mathematics to analyze the human comfort，and the annoyance is used as the evaluation index. The results show that under the train operation，the vibration level of the throat area is the highest，and the vibration stability for different areas of the depot is as follows：the inner area > the throat area > the upper cover area. As the lateral propagation distance increases，the maximum Z vibration level of the platform decreases approximately linearly. The vibration peak of each building on the top is 12.5~20 Hz，and the main frequency band of secondary noise is 63~80 Hz. The vibration and secondary noise of each building attenuate with the increase of floors，but the vibration and secondary noise of residential building are amplified after 12 floors. The vibration of each building on the upper cover do not exceed the standard，but the secondary noise exceeds the standard in the commercial building，and the maximum exceeding value is 4.9 dB（A）. The annoyance results obtained by the joint annoyance model are in good agreement with the standard evaluation results，but the annoyance can refine the influence of vibration and secondary noise on human comfort. The model calculation shows that although the first floor of the residential building meets the standard limit，the joint annoyance is above 0.6 at night，and the evaluation using the joint annoyance rate model is more demanding.

Vibration limit is one of the most essential contents in vibration serviceability research. Former studies showed that many factors, such as biological and environmental factors, significantly affected vibration limits deeply. As a reason of defects in traditional research, such as small scale data and unreal test environment, quantitative relationships between vibration limits and these factors stayed unknown. Based on data collected by crowd sensing in real environment, crest factor of vibration/ BMI/ human age/ floor of building were found key factors by using maximal information coefficient (MIC) in coefficient analysis. Functional relationship and 95% confidence intervals between vibration limits and key factors were proposed, respectively. Lilliefors test and normal probability plot show that residuals between fitted values of limits and measured ones follow a normal distribution. A novel approach of estimating vibration serviceability based on probability is proposed when key factors and vibration magnitude are known.

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.

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.

In the dynamic impact response analysis of the structure，the dynamic amplification factor（DAF）is usually used to simplify the calculation of the dynamic response of the structure. However，the size of DAF in engineering structures is still controversial. In order to solve this problem，the analytical expression of DAF of multi-degree-of-freedom system（MDOF）is derived in this paper，and the precondition of DAF greater than 2.0 is analyzed. The accuracy of the analytical expression is verified by the single-degree-of-freedom（SDOF）and MDOF example models，and the reason why the DAF of the MDOF is greater than 2.0 is explained. Finally，based on the DAF analytical method proposed in this paper，the DAF distribution law of beam string under cable breaking impact is analyzed. The analysis results show that when the contribution of a first-order modal shape is opposite to the static response，the DAF of the beam string may be greater than 2.0. Even for the damping system，the DAF of the beam string may be greater than 2.0.