Based on the basic principle of variational method and limit equilibrium method，the stability of soil slope under earthquake action is analyzed accurately. Combining the limit equilibrium method of slope stability analysis and the pseudo-static method，the auxiliary functional under constraint conditions is constructed by introducing Lagrange multiplier into the equilibrium equation of sliding soil. The first order ordinary differential equations with the basic unknowns of potential sliding surface，normal stress of sliding surface，force of sliding body，safety factor and Lagrange multiplier are obtained by using Euler equation. The coupled nonlinear differential equations are solved numerically by using the shooting method，and an accurate solution for slope stability analysis under seismic action is obtained. The effectiveness of the model and method is verified by numerical examples.

Laser powder bed fusion (LPBF) technology, a cutting-edge process in metal additive manufacturing, has been successfully applied in high-end manufacturing sectors like aerospace. However, strong multi-physical field coupling effects frequently lead to dynamic instability in the molten pool, causing widespread porosity defects within fabricated parts and severely impacting forming quality stability. Traditional monitoring methods face limitations such as high cost and deployment difficulties, struggling to meet industrial production demands. To address these challenges, this paper proposes an online monitoring and intelligent internal quality discrimination method based on acoustic emission (AE)-deep learning fusion. An AE sensor-based online monitoring system for the LPBF process was developed. By continously monitoring AE signals throughout the entire process, the mapping relationship between AE signal characteristics and forming quality was revealed, creating a molten pool AE dataset comprising over 80,000 samples. To tackle the difficulty of extracting weak fluctuation features from the molten pool, a frequency domain feature extraction network based on the adaptive Fourier neural operator (AFNO) and a high-dimensional feature mapping classifier based on the Kolmogorov-Arnold network (KAN) were constructed. This approach analyzes molten pool dynamic characteristics through a multi-scale time domain feature fusion mechanism. By precisely mapping high-dimensional features using high-dimensional manifolds, the method achieves enhanced characterization of weak fluctuation features in AE signals and high-precision quality discrimination. Experimental results demonstrate that developed monitoring system effectively captures the dynamic behavior of the molten pool, and the proposed method achieves a quality discrimination accuracy exceeding 97%.

Liquid rocket engine turbopumps operate under severe non-stationary conditions, making it challenging for traditional vibration signal analysis methods to effectively extract fault features. To address this challenge, the cyclostationary random signal model is extended and a generalized cyclostationary analysis framework is established. This framework preserves the advantages of cyclostationary methods in fault diagnosis while broadening their applicability to non-stationary operating regimes. Focusing on vibration signal modeling, fault feature extraction, and characterization, a comprehensive generalized cyclostationary analysis framework specifically for rocket turbopump fault diagnosis is proposed. The superiority and validity of the established theoreical system are demonstrated through a cryogenic bearing operation experiment on a rocket turbopump and a cavitation fault simulation test on a centrifugal pump. Results indicate that vibration signals from rotating machinery can be regarded as approximately cyclostationary processes subject to time warping, which can be further transformed into modulated cyclostationary signals. In the rocket turbopump cryogenic bearing operation experiment, fault feature signals are extracted using the proposed blind adaptive cyclostationary-nonstationary signal extraction method. Its order-frequency spectral correlation map clearly detects spectral lines corresponding to the fundamental train frequency (0.42 Hz) and the ball pass frequency outer race (5.08 Hz). In the centrifugal pump cavitation fault simulation experiment, the proposed high-precision reassigned spectral correlation estimation technique enhances the localization of the blade-pass frequency (197 Hz) in the spectral correlation map. Furthermore, it reliably identifies fault features even under severe noise induced by increasing cavitation levels.

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.

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.

To effectively detect and localize vibration faults such as clamp loosening in aero engine accessory pipes, this paper proposes a novel diagnosis method based on dynamic responses of accessory pipes and transmissibility functions. The dynamic model and equation of the faulty pipe are established based on the principle of structural similarity and dynamic similarity, where the impact of the fault is simulated as an additional nonlinear load acting on related position of the pipe. Through the analysis of structural dynamic characteristics, the relationship between dynamic responses and fault occurrence and location is derived and analyzed, a fault diagnosis method based on transmissibility function is then proposed, and the operating process of the method is summarized as well. The accuracy and practicality of the proposed fault diagnosis method are verified through multiple experimental examples. Theoretical analysis and testing results show that the diagnostic method proposed in this paper can accurately detect and localize the existence and position of the clamp loosening fault. Meanwhile, this diagnostic method is suitable for single and multiple clamp loosening faults in aero engine accessory pipes.

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.

Traditional bearing fault diagnosis methods often suffer from low accuracy and weak model generalization under varying working conditions due to diverse sample distributions, scarcity of fault samples, and limited feature extraction capabilities of some few-shot learning algorithms. To address these challenges, this paper proposes a novel method for variable condition bearing fault diagnosis that combines a squeeze-and-excitation residual network (SE-ResNet) with meta-transfer learning (MTL). One-dimensional bearing vibration signals collected under different working conditions are converted into time-frequency images using continuous wavelet transform (CWT), thereby transforming the bearing fault diagnosis task into an image recognition problem. A squeeze-and-excitation (SE) attention mechanism is introduced to construct an SE-ResNet backbone network model. This focuses on more effective feature channels, thereby enhancing feature extraction and representation capabilities. Leveraging the advantages of transfer learning (which provides robust initial deep network parameters) and meta-learning (which enables rapid adaptation), the model undergoes sequential pre-training and meta-transfer training. This process yields a high-precision meta-transfer network that can be fine-tuned with only a small number of samples, ultimately achieving accurate bearing fault diagnosis under variable working conditions. The proposed method is validated using two benchmark datasets and a bearing fault simulation test bench developed in the laboratory. Comparative analysis with other methods demonstrates that the proposed method exhibits higher recognition accuracy and superior generalization performance for bearing fault diagnosis under both few-shot and variable working conditions.

Bearings are critical bogie components, making their early fault detection particularly important. This paper proposes an early fault detection method for bearings based on a relation network (RN). A health status detection relation network model is designed to effectively extract bearing condition features and measure the nonlinear distance between these features. In the offline modeling phase, normal samples from the bearing are collected for training, allowing the model to learn the nonlinear distances among the healthy state sample features. During the online monitoring phase, samples from the current operating state are acquired, and a relation score is obtained as a health indicator for the bearing condition. The 3σ criterion is then applied to determine the health indicator threshold for detecting the bearing health status and identifying faults promptly. Experiments were conducted on the XJTU-SY rolling bearing full-lifecycle dataset. Results show that, compared to methods like root mean square, kurtosis, and stacked autoencoders, the health indicator of the proposed method is more sensitive to early faults and exhibits better monotonicity and trend. Furthermore, in comparison with methods such as Isolation Forest, Support Vector Machine, and stacked autoencoders, the proposed method detects the first fault occurrence earlier, demonstrating considerable practical value.

To address the challenges of fully characterizing wheel information and accurately locating and quantifying wheel damage using trackside signals, this paper proposes a multi-sensor data fusion algorithm combined with an improved convolutional neural network (CNN) for wheel tread defect identification. A vehicle-track dynamics coupling model is established based on multi-body dynamics and finite element theory. By strategically arranging fewer sensors, multimodal features are extracted, and data fusion algorithms are optimized for parameters like wheel geometry and vehicle speed. An improved CNN model is then proposed, building upon both 1D-CNN and 2D-CNN architectures. Simutaneously, frequency domain features and image features are fused, leading to a new CNN algorithm model that incorporates these fusion features. Defect feature extraction is performed on the reconstructed signal, and the improved CNN, leveraging the fused data features, is used to achieve wheel damage identification. The effectiveness of the proposed method is validated using both simulation data and actual case studies, in conjunction with a proportional vehicle test rig. The damage identification performance of the proposed model is compared against CNN, BP neural network (BPNN), and support vector machine (SVM) under various signal test sets and data features. Results indicate that the proposed damage identification model can more effectively identify wheel tread defects, showing good consistency with measured results. Fusing data features from different dimensions can characterize defects under varying degrees of damage and significantly improve identification performance. This approach successfully addresses issues where trackside data alone cannot fully reconstruct wheel status, thereby providing crucial technical support for the online damage identification of wheel defects.