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With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively.

The fatigue strength of a component is known to highly depend on its surface quality, and it is thus necessary to develop a reliable and appropriate mathematical model for fatigue strength assessment that consider the effect of surface roughness. In this paper, different underlying physical mechanisms of the roughness effect at different regions of specimens were studied by fatigue testing of 7N01 aluminum alloy. For a quantitative analysis of the surface roughness effect, a revised stress field intensity approach for a fatigue strength assessment of microsized notches was proposed as a theoretical support. In the new model, a new form of weight function was built to adapt the characteristics of microsized notches. In addition, the effect of the field radius was fundamentally weakened on solution of the stress field intensity and the difficulty of fatigue failure region definition in the traditional method was overcome correspondingly in the proposed model, which made the calculated field strength accurate and objective. Finally, to demonstrate the validity of the revised approach quantitatively, specimens with conventionally sized notches were subjected to stress field intensity calculations. The results showed that the revised approach has satisfactory accuracy compared with the other two traditional approaches from the perspective of quantitative analysis.

The test rig can simulate real service conditions to obtain the friction and wear evolution of the bushing under high temperature and complex loading conditions, providing important experimental methods for material optimization, structural design improvements, and service life prediction of the bushing. The Variable Stator Vane (VSV) system is a critical component in aircraft engines, with its bushing providing structural support and lubrication. Under high temperatures, complex loads, and periodic motions, the bushing is prone to wear, which can affect system performance. In this study, a friction and wear test rig was designed to simulate realistic VSV bushing operating conditions. The rig is equipped with a programmable reciprocating drive, adjustable radial and bending moment loading, and a closed-loop temperature control system, allowing the wear process to be reproduced under high-temperature and complex loading conditions. Friction torque is measured using a torque sensor, while the equivalent wear volume is calculated from real-time data collected by two position sensors. Six samples were tested under 250 °C, 300 °C, and 350 °C, with bending moments of 1.5 Nm and 3 Nm, and a radial load of 30 KN, for 15,000 cycles. The results show that friction and wear evolve in two distinct stages: in the initial stage, friction torque and wear increase rapidly, followed by a slower growth rate during the stable stage. Higher temperatures and larger loads result in greater peak friction torque and more severe early wear. This study provides experimental methods to support VSV bushing material optimization, structural improvements, and lifetime prediction.

Abstract Model-based systems engineering is widely used in aviation and maritime industries for complex system integration. However, engineering machinery design prioritizes rapid market-driven development. This paper proposes an agile MBSE model balancing speed and quality, structured in three stages: requirement analysis (SysML-based formalized modeling), architecture design (functional and traceability analysis), and instance validation (simulation-driven configuration trade-offs). Using winch design as a case study, the model enhances efficiency by synchronizing iterative requirements with systematic verification, outperforming traditional methods in reducing design cycles while maintaining robustness.

The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited generalization, low efficiency, and reliance on static rules. This paper proposes PPM-SACG, a privacy preference matrix-based model for sensitive attribute classification and grading. The model adopts a three-stage architecture: (1) composite sensitivity metrics are derived by integrating information entropy and group privacy preferences; (2) domain knowledge-guided clustering and association rule mining improve classification accuracy; and (3) mutual information-based hierarchical clustering enables dynamic grouping and grading, incorporating high-sensitivity isolation. Experiments using real-world vehicle management data (50 attributes, 3000 records) and user privacy surveys verify the method’s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance.

Time-varying reliability models of multi-cracked beam structures are established in this paper, which provide a theoretical method for the safety evaluation of multi-cracked beam structures. The reliability models proposed in this paper consider the interaction between the complex statistical correlations between system parameters during system operation and maintenance correlations, which is a difficult problem in the time-varying reliability modeling that takes into account work mechanisms and maintenance behavior. In the proposed models, multiple cracked elements are regarded as a dependent series system. The stresses, crack extensions, and multiple failure modes between each element constitute the complex failure dependence of the system. The time-varying reliability models of a multi-cracked beam structure are established via the neural network method and failure dependence analysis. Moreover, the failure dependence coefficient is proposed to quantify the time-varying failure dependence. Based on the working principle of the beam structures and the maintenance mechanism for the cracked state of the beams, a time-varying system reliability mode considering the maintenance dependence is proposed. Furthermore, the maintenance dependence coefficient index is proposed to quantitatively measure the interaction between the maintenance dependence and the failure dependence. Finally, the validity of the model is verified through the Monte Carlo simulation method. In the numerical examples, the relationship between maintenance dependence and failure dependence is illustrated and the influences of the statistical characteristics of the maintenance characteristic parameters on the maintainability and failure dependence are analyzed.

We aimed to estimate the association of sleep, sedentary activity and physical activity with cognitive function among older adults, with consideration of the competing nature between variables of activity status. Cross-sectional study. A total of 3086 older adults (60 years or older) in the 2011–2014 National Health and Nutrition Examination Survey were included. The Global Physical Activity Questionnaire was used to measure self-reported time for sedentary activity, walking/bicycling and moderate-to-vigorous physical activity (MVPA). Cognitive function was examined using the CERAD Word Learning subtest (memory), Digit Symbol Substitution Test (executive function/processing speed), and Animal Fluency Test (language). Sleep duration was obtained via interview. Isotemporal substitution models using multivariable linear regression were applied to examine the associations of replacing sleep, sedentary activity, walking/bicycling, MVPA with each other and cognitive function, stratified by sleep duration per night (≤7h, >7h). Among participants with sleep duration ≤7h/night, replacing 30min/day of sedentary activity with 30min/day of MVPA or 30min/day was associated with better cognition. Among participants with sleep duration >7h/night, replacing 30min/day of sleep with 30min/day of sedentary activity, walking/bicycling, or MVPA was associated with better cognition. Replacing sedentary activities with MVPA was associated with favorable cognitive function among older adults sleeping no longer than 7h/night, and replacing excessive sleep with sedentary or physical activities was associated with favorable cognition. Future research is expected to examine the associations of replacing different activity status on long-term cognitive outcomes in longitudinal studies.

To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively.

Only few failure data can be collected because of increasing costs and reliability. Therefore, improving the estimation accuracy of Weibull distribution with small samples has become an urgent problem. In this study, a method for estimating the Weibull parameter and analyzing reliability is proposed based on the successive approximation method. In this method, the effect of sample size on the location parameter is considered to establish an iterative model of the location parameter. The iterative equations of scale and shape parameters are constructed based on the location parameter and failure data. Then, an extensive Monte-Carlo simulation study is conducted to compare the performance of the proposed method with the existing methods. Simulation results show that the proposed method is superior for estimating Weibull parameters and reliability in terms of precision, robustness, and efficiency. Finally, two real examples are analyzed to illustrate the application of the proposed method.

Objectives: We aimed to examine how COVID-19 incidence is associated with depressive symptoms in China, whether the association is transient, and whether the association differs across groups. Methods: We used a longitudinal sample from 2018 to 2020 waves of the China Family Panel Study. We constructed COVID-19 incidence rates as the number of new cases per 100,000 population in respondents’ resident provinces in the past 7, 14, and 28 days when a respondent was surveyed. We performed linear or logistic regressions to examine the associations, and performed stratified analyses to explore the heterogeneity of the associations. Results: Our sample included 13,655 adults. The 7-day incidence rate was positively associated with the CES-D score (coef. = 2.551, 95% CI: 1.959–3.142), and likelihood of being more depressed (adjusted odds ratio = 6.916, 95% CI: 4.715–10.144). The associations were larger among those with less education, pre-existing depression, or chronic conditions. We did not find any significant association between the 14- or 28-day local incidence rates and depressive symptoms. Conclusion: The impact of COVID-19 incidence on mental health in China’s general population was statistically significant and moderate in magnitude and transient. Disadvantaged groups experienced higher increases in depressive symptoms.