This study addresses the challenge of fault prediction in smart lamp post systems. It proposes a fault-prediction and intelligent decision-making framework (DSADN) based on an improved machine-learning algorithm. By integrating a support vector machine (SVM) optimized with a kernel function, a long short-term memory (LSTM) enhanced with attention, a graph neural network, and a Transformer architecture, a multimodal feature extraction channel is constructed. By integrating the physical-constraint mechanism for domain knowledge embedding with the three-stage adaptive training strategy, the model’s robustness in complex environments is significantly enhanced. Experiments show that the system achieves an LED module attenuation prediction accuracy of 96.3% on 326,784 multi-source data samples, with an F1 score of 0.932. The performance degradation rate in extreme environments is only 7.6%, which is more than 35% lower than that of traditional models. Edge deployment achieves a 15 ms real-time response and reduces memory usage by 62%. The research results address three major problems of the existing smart light pole system: low detection efficiency, high false alarm rates during monitoring, and prediction lag. It also provides an efficient solution for the predictive maintenance of smart city infrastructure.
The operational use of wheeled land transport vehicles and their on-board systems exposes them to significant vibratory stresses, particularly during motion. These vibrations, stemming from irregular road surfaces, exhibit complex non-Gaussian behaviour. To accurately assess their impact on mechanical systems and design robust qualification tests, the AFNOR X50-144-3 Edition 2 standard provides a framework for analysing mechanical environments. This method involves classifying measured data by stationarity and deriving statistical distributions to model vibratory effects. This study introduces key innovations to this methodology. A novel classification technique is proposed, leveraging spectral content similarity to enhance data segmentation. Additionally, new a priori statistical models are developed to extrapolate short-duration measurements into long-term system lifespan profiles. The effectiveness of these advancements is demonstrated through their application to real-world measurement data and synthetically generated signals with controlled spectral properties. This work represents a step forward in vibration analysis, offering improved accuracy for mechanical environment assessments in land vehicle engineering.
Using a self-designed friction testing machine, this study investigates the effect of different current densities on the friction coefficient in the rounded-corner area of AA7075 aluminum alloy sheets under dry friction conditions. Experimental data were processed and curve-fitted in Origin to establish a variable-friction-coefficient model for the fillet zone under different current densities. Finite element simulations were performed in ABAQUS, and the variable-friction law was implemented through the VFRIC interface to compare it with the constant-friction model and experimental results. The thickness-distribution patterns of the two models were compared with those from the experimental validation. The results show that the friction coefficient of the fillet zone decreases with increasing current density from 0 to 10 A/mm 2 and increases from 10 to 18 A/mm 2 . Based on the variable-friction model in the rounded-corner area under different current densities, the data-fitting accuracy is better; the simulation and example-verification results are consistent, and the maximum thinning of the aluminum alloy sheet occurs at the rounded corner of the punch. The results show that the friction coefficient in the fillet zone decreases with increasing current density from 0 to 10 A/mm 2 and then increases from 10 to 18 A/mm 2 . Compared with the constant-friction reference case, the variable-friction model provides better agreement between the simulated and experimental thickness-distribution trends, and the maximum thinning occurs near the punch corner.
In academia, research on gear units is increasingly focused on electrification, higher rotational speeds, lightweight architectures and stricter efficiency and acoustic constraints. Advanced multiphysics modelling now incorporates contact mechanics, structural flexibility, lubrication, thermal behaviour and electromechanical coupling. Significant advances have been made in the fields of dynamics, vibroacoustic, Rolling Contact Fatigue modelling and coupled power losses and thermal analyses. Furthermore, numerical tools facilitate analyses at the system-level as opposed to the component level. Within the industrial sector, transmissions are required to demonstrate an increase in power density, durability and efficiency with reduced noise, a requirement that is particularly pertinent in the context of electric transmissions. At the same time, manufacturing trends include enhanced precision, surface superfinishing, dry machining and full digitalisation with digital twins. Health monitoring is evolving towards hybrid strategies. Sustainability considerations now influence materials, lubrication strategies, eco-design, repairability and life cycle optimisation. In the field of education, transmission engineering remains fundamental. It is evolving towards systemic, multiphysical and digital approaches. Study programmes are increasingly incorporating tribology, Noise, Vibration & Harshness, thermal modelling, durability prediction, optimisation and data-based monitoring, alongside close links between research laboratories and industry. Digital skills, simulation tools and interdisciplinary training are becoming essential. Overall, far from being a mature or declining field, gear transmission engineering is undergoing a profound transformation. Its future is driven by high-speed electrified systems, integrated modelling, predictive maintenance, sustainable design and enhanced collaboration between research, industry and education.
Determining the characteristics of impact forces is a key component of structural health monitoring systems, as it allows for continuous assessment of the structural integrity of engineered systems. This study addresses two complementary inverse problems related to impact force identification. The first focuses on the reconstruction of the impact force when the impact location is known, while the second deals with the localization of the impact when the position of excitation is unknown. An analytical model was developed to compute the transfer matrix, taking into account different boundary conditions, represented by both rigid and elastic supports, to evaluate their influence on reconstruction accuracy. The impact force was reconstructed by using the regularization methods and comparing two techniques: the TGSVD method and the Tikhonov method. For the latter, two optimization strategies for the regularization parameter λ were considered. The results demonstrate that the S-curve approach provides a more accurate and stable estimation of the impact force. For the localization problem, an optimization-based approach was implemented to estimate the impact position by minimizing the discrepancy between measured and predicted responses. The findings highlight the significant influence of support stiffness on both the accuracy of force reconstruction and the precision of impact localization, emphasizing the importance of appropriate boundary modeling in inverse dynamic analysis.
