As core assets in modern military and civil aviation, aviation equipment features high complexity, long service lifecycles, and stringent reliability requirements, posing severe challenges to equipment development and operation maintenance. Digital twin technology provides effective support for the full-lifecycle management of aviation equipment. However, conventional digital twin technologies remain confined to static representation, making them insufficient to support deep cognition, evolutionary prediction, and intervention-oriented decision-making for the complex and dynamic behaviors of equipment throughout its lifecycle. In this context, driven by rapid advances in cognitive intelligence technologies such as large models, knowledge graphs, and causal reasoning, cognitive digital twins are emerging as a new research frontier. Existing studies on cognitive digital twins are largely limited to localized explorations within specific scenarios. Their theoretical system remains underdeveloped, and a unified architecture for complex aviation systems is lacking. In view of these gaps, a novel cognitive digital twin framework for the full-lifecycle management of aviation equipment is proposed, aiming to elucidate the paradigm shift from state mapping to autonomous cognition. First, it reviews the evolution of digital twin technology from “virtual-physical mapping” to “intelligent cognition”, and analyzes the current applications and urgent needs of this transition in the aviation domain. Second, the cognitive digital twin framework is deconstructed into its constituent layers—physical entity, virtual entity, interactive connection, and cognitive embedding. It also provides a detailed explanation of the cognition-enabling mechanisms of cognitive digital twins across the full lifecycle of the equipment. Subsequently, it systematically summarizes the research status and technical pathways of each layer from an implementation perspective. Finally, in response to current challenges faced by the digital twin application of aviation equipment, it explores the integration of emerging technologies including large models, and looks ahead to future trends in constructing a cognitive digital twin system for aviation equipment with deep comprehension, autonomous evolution, and trustworthy decision-making capabilities.

The accuracy of reliability assessment for harmonic drives in industrial robots depends on the extent to which accelerated degradation models (ADMs) can reflect actual service conditions and on the validity of accelerated degradation testing methods. However, existing ADMs and test methods often fail to adequately characterize the periodic shock loads experienced by harmonic drives under complex operating conditions, such as variable speed and load, forward-reverse switching, and abrupt starts and stops. As a result, the degradation behavior of harmonic drives is difficult to characterize accurately, which undermines the credibility of reliability assessment results. To address this issue, this study develops a novel ADM for harmonic drives that explicitly incorporates periodic shock loads. Periodic step shocks are introduced into a Wiener process to capture abrupt performance changes induced by shocks during operating-condition transitions. In addition, a shock influence factor is incorporated into a power-law acceleration model to establish the relationship between shock loads and degradation rate, and the resulting ADM is formulated based on the acceleration factor constant principle. To ensure model accuracy, a hybrid parameter estimation approach combining Latin hypercube sampling and a genetic algorithm is employed for high-dimensional nonlinear estimation, and the 95% confidence intervals of the model parameters are obtained. Furthermore, a service load spectrum-driven accelerated degradation testing method is proposed based on measured load spectrum data from industrial robots. Following the principle of equivalent torque consistency, a multi-level accelerated loading spectrum is designed to simulate variable-speed, variable-load, and shock conditions. Accelerated degradation tests are then conducted on three harmonic drives to obtain transmission error degradation data for reliability assessment. The results show that the proposed model provides higher reliability assessment accuracy than existing models and enables more accurate prediction of the service life of harmonic drives.

The strong time-varying working condition excitation under random wind loads on gear transmission system of wind turbine leads to the switching of the lubrication state between gear teeth and affects the dynamic parameters，and the final dynamic response. Existing studies have overlooked the significant changes in gear dynamic parameters under different lubrication states and the resulting changes in meshing dynamic characteristics，as well as the complex coupling behavior that feeds back to the lubrication state，affecting the accuracy of dynamic analysis. A multi-degree-of-freedom dynamic differential equation group for the translational-torsional coupling of the wind turbine gear transmission system is firstly established，and the expression of the dynamic oil film thickness between gear teeth to determine the lubrication state of the meshing pair is derived. Then，the key dynamic parameters such as contact stiffness，damping coefficient，and friction coefficient under different lubrication states are modeled. Subsequently，the real-time relative elastic deformation of the meshing pair is solved to update the dynamic oil film thickness，forming an iteration. Based on this dynamic response-lubrication state coupling model，the dynamic characteristics and lubrication characteristics of the wind turbine gear system are analyzed. The research shows that compared with constant working conditions，time-varying working conditions significantly increase the amplitude of the meshing pair and the frequency of lubrication state switching. Compared with the meshing of the sun gear and planet gear，the meshing of the high-speed helical gear is less affected by time-varying working conditions. The surface roughness of the gear teeth also plays a role. When the roughness increases，the meshing pair is more likely to enter the boundary lubrication state，and causes the vibration amplitude to increase. The research results can provide a theoretical basis for the condition detection and fault diagnosis of the wind turbine transmission chain.

During the meshing process, the gear pairs often deviate from the theoretical meshing line, leading to corner contact mesh impacts. Under fault conditions, corner contact impacts increase significantly and form coupled excitation with fault impacts, influencing the vibration response of the planetary gear train. However, existing phenomenological models are found difficult to characterize this coupled impact excitation. For this reason, corner contact forces and stiffness variations in gear pairs under both healthy and faulty conditions are analyzed through the corner contact force and stiffness models, respectively. A meshing impact function is derived based on the meshing phase relationship, establishing a key link that characterizes the time-sequence correlation between corner contact forces, fault impact forces, and the meshing cycle. Based on this meshing impact function, a phenomenological model under the coupled excitation of corner contact mesh - fault impacts is developed. The matching synchrosqueezing transform method is employed to analyze the response characteristics under the impact excitation. Simulation and experimental signal analysis results under different fault sizes show that sideband amplitude and time-frequency energy increase with growing fault size. Capitalizing on this phenomenon, the fault evolution patterns is quantitatively characterized using FM0 and MSET energy spectra. The concordant variation patterns observed in simulation and experimental results validate the effectiveness of the proposed model in characterizing coupled impact excitation.

The metro door actuation system is a typical multi-speed electromechanically coupled actuation system that frequently undergoes state transitions during operation. It exhibits pronounced nonlinearity and time-varying dynamics and is highly sensitive to environmental disturbances and passenger behavior，making accurate short-term prediction of key behavioral variables challenging. To enhance the prediction capability of short-term transient behaviors and the anticipatory control capability of the system，a behavior digital twin model is developed that integrates physics-based mechanism modeling with data-driven learning，enabling prediction of key behaviors—such as motor current and door displacement—within a 0-0.5 second horizon. First，an electromechanically coupled dynamic model is developed based on torque balance，transmission efficiency，and load response，providing a high-fidelity representation of the system’s motion behavior. Second，a hidden state identifier based on a pattern search algorithm，together with an action-cycle-level model-parameter updating mechanism，is proposed to achieve equivalent inverse estimation of the state parameters. Third，a physics-guided deep learning prediction model (PG-DLPM) is constructed by integrating a temporal convolutional network with a transformer encoder，improving both prediction accuracy and model interpretability. Finally，a door-motion-based consistency metric is developed，and the digital twin is driven by monitoring signals (e.g.，motor current and lead-screw rotation angle) to generate high-fidelity state-parameter trajectories，which are fused with measured data as inputs to PG-DLPM for high-accuracy short-term transient prediction. Experimental validation using field-measured data collected from a metro line shows that the proposed twin model exhibits high consistency with the physical system in dynamic behavior. Compared with state-of-the-art time-series forecasting models，the proposed PG-DLPM reduces the average root-mean-square error and mean absolute error by 37.45% and 46.05%，respectively，while their corresponding standard deviations decrease by 63.13% and 79.01%. These results demonstrate superior predictive accuracy and robustness，supporting intelligent control and decision-making for metro door actuation systems.

Health monitoring and early fault detection of rotating machinery are crucial for ensuring equipment operational safety. Traditional fault feature extraction methods often rely on preset parameters, limiting their effectiveness in practical applications. Although the Ramanujan Periodic Transform can effectively separate periodic components in signals by constructing orthogonal subspaces, its performance heavily depends on high-fidelity fault simulation models, which typically require prior knowledge of fault features, making it difficult to generalize in scenarios lacking fault samples. To address this issue, a digital twin architecture integrating conditional diffusion models and the Ramanujan Periodic Transform (Diffusion-Ramanujan) is proposed. This method employs a classifier-free guided conditional diffusion model to transform unknown-type fault samples into healthy samples. By comparing the distribution differences in the envelope spectra before and after transformation, potential fault features are automatically mined. Based on the extracted features, a phenomenological simulation model is constructed as a digital twin to drive the Ramanujan Periodic Transform, enabling parameter-independent fault feature separation from monitoring data and the establishment of health indicators for equipment health monitoring. Experimental results on the IMS bearing dataset demonstrate that the proposed method can effectively identify early faults without prior fault knowledge, achieving earlier fault detection time and higher monitoring accuracy compared to baseline models such as Improved Envelope Spectrum and Anomaly-Transformer. This provides an effective pathway for rotating machinery health management that does not rely on preset parameters or prior knowledge.

Conventional evaluation methods of structural performance in gear-transmission systems rely on offline finite-element analysis, which is time-consuming and cannot satisfy the timeliness requirements of online condition monitoring in high-end equipment. To overcome this limitation, an edge-cloud collaborative digital-twin technique is proposed for online condition monitoring. A digital-twin framework containing physical, edge, cloud, and communication layers is established with clearly defined function and road-map of each layer. A graph neural network-based surrogate model is developed to rapidly predict structural performance, addressing the unstructured mesh topology of finite-element models. An online condition monitoring platform is implemented on a gear transmission test bench, integrating real-time signal acquisition, structural prediction, and cloud-based visualization. Results demonstrate that the surrogate model achieves more than 90% consistency with offline finite-element analyses with a total response time within 4 s, enabling a rapid and accurate structure monitoring of core components in gear transmission systems, and providing a feasible new approach for real-time condition monitoring of high-end equipment.