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Energy relief effect of real-time drilling to prevent rockburst in high-stress rock 认领 引用 被引量：1
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作者 Zhichao He Fengqiang Gong +2 位作者 Li Ren Weimin Yang Xuezhen Wu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1460-1475,共16页
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens... To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks. 展开更多
关键词 Rock mechanics Rockburst Real-time drilling Drilling energy relief Energy storage capacity Rockburst proneness
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Real-time monitoring of tunnel structures using digital twin and artificial intelligence:A short overview 认领 引用
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作者 Mohammad Afrazi Danial Jahed Armaghani +2 位作者 Hossein Afrazi Hadi Fattahi Pijush Samui 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期315-330,共16页
Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin... Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin(DT)technology provide innovative solutions for the real-time monitoring of tunnel systems,suggesting proactive maintenance tactics and improved safety protocols.This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance.We explore the core concepts of AI and DT and their applicability to structural monitoring,encompassing machine learning,computer vision,and sensor integration.Through the utilization of these AI-powered technologies,engineers are equipped with unparalleled insights into the state and behavior of tunnels,facilitating the early identification of irregularities and the optimization of maintenance timelines.We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems,emphasizing their foundations,benefits,and practical uses.Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings.Moreover,we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them,such as data augmentation,interpretable AI,edge computing,and continuous monitoring.Ultimately,the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift,offering substantial advantages over conventional techniques.By adopting AI-driven monitoring systems,tunnel operators can augment safety,prolong the lifespan of infrastructure,and decrease operational expenses,molding the future of subterranean infrastructure management. 展开更多
关键词 artificial intelligence digital twin machine learning monitoring real-time tunnelling
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Real-time control for autoclave curing process of CFRP composites considering tool-part interaction 认领 引用
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作者 Wenyuan TANG Liang HE +4 位作者 Xinyu HUI Jianwen NIU Yingjie XU Rutong YANG Yutong LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期667-681,共15页
An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinfo... An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinforced Polymer(CFRP)composites.First,a high-fidelity Finite Element(FE)model incorporating tool-part interaction is developed to reveal the curing process of the composites,wherein the interaction is characterized by friction interface modeling with experimentally measured cure-dependent friction coefficients.The accuracy of FE model is confirmed through experimental tests on a doubly curved T-stiffened composite panel.This validated model then generates a dataset of curing temperature profile and associated defect information,which is used to train a customized Long Short-Term Memory(LSTM)neural network.We culminate in a real-time control framework that actively optimizes the curing process by integrating LSTM-based state prediction with Q-learning-driven decision logic.The optimized thermal profile demonstrates a clear performance enhancement over the traditional multi-dwell approach,achieving marked reductions in temperature difference,Degree of Cure(DoC)difference and tool-part interface shear stress,which provides more insights for intelligent composite manufacturing. 展开更多
关键词 CFRP composites Curing process Neural network Real-time optimization Tool-part interaction
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Mechanical property variation mechanisms of granite subjected to real-time high temperatures and subsequent cooling treatment 认领 引用
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作者 Rui Pang Dehao Meng +6 位作者 Thomas Frühwirt Hao Liu Yanan Zhao Qingyou Zhu Wengang Dang Mohamed Ismael Fei Wang 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第1期277-295,共19页
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest... During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures. 展开更多
关键词 granite nonlinear cooling real-time high temperature thermal cracking thermo-mechanical coupling
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Real-time impedance spectroscopy of lithium-ion batteries via composite current pulses 认领 引用
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作者 Yue Yang Tongfang Fu +4 位作者 Wenbin Li Paul Thiele Chenye Song Simon Mertes Stefan Pischinger 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第6期902-914,I0021,共13页
Conventional sinusoidal electrochemical impedance spectroscopy is often impractical for real-time control or on-board diagnostics because measurements at low frequencies require long dwell time,resulting in lengthy te... Conventional sinusoidal electrochemical impedance spectroscopy is often impractical for real-time control or on-board diagnostics because measurements at low frequencies require long dwell time,resulting in lengthy test duration.To address this issue,the composite current pulse excitation is implemented in this work for real-time impedance spectrum acquisition,using the discrete Fourier transform.Pulse sequences and sampling conditions are designed to balance bandwidth and accuracy of the impedance results while satisfying hardware constraints and system relaxation requirements.To improve repeatability under noise and dynamic operating conditions,outliers are mitigated by introducing truncated singular value decomposition reconstruction.Two pulse widths(1 and 100 ms)are applied to overcome the bandwidth limitation of a single-width excitation,enabling an accurate spectrum across 1 k Hz to 1 Hz within~1 s.On a commercial 18650 lithium-ion battery,a mean relative impedance deviation of 2.1%compared with galvanostatic electrochemical impedance spectroscopy results is achieved across state of charge from 5%to 90%at 10 and 25℃.Time-domain voltage simulations using pulse-calibrated parameters reproduce the measured dynamic responses,achieving accuracy comparable to simulations parameterized from galvanostatic electrochemical impedance spectroscopy. 展开更多
关键词 Real-time impedance measurement Current pulse Electrochemical impedance spectroscopy Lithium-ion batteries
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Thermodynamic real-time coupling modeling and thermal characteristic analysis of angular contact ball bearings under combined loads 认领 引用
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作者 Wenbing Tu Yuankuo Lou +1 位作者 Chenlu Liu Wennian Yu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期518-534,共17页
The distribution of heat generation and operating temperature are crucial factors affecting the performance and useful service life of angular contact ball bearings.A thermodynamic real-time coupling analytical model ... The distribution of heat generation and operating temperature are crucial factors affecting the performance and useful service life of angular contact ball bearings.A thermodynamic real-time coupling analytical model is established in this work to illustrate alterations in the bearing structure,internal load,and the sliding and rotation of the ball correlated with temperature fluctuations.The temperature rise and heat generation of angular contact ball bearings under combined loads are accurately calculated.The simulation of the temperature rise in bearings is conducted based on this model,and the effectiveness of the model is validated through simulation experiments.The simulation results of the distribution of heat generation are compared between thermodynamic real-time coupling and thermodynamic sequential coupling,with a focus on the transient thermal characteristics of bearings and the effect of load on their steady-state thermal characteristics.The precision of temperature simulation through thermodynamic real-time coupling is significantly higher than that through thermodynamic sequential coupling.Axial load mainly affects heat generated by the sliding and rotation of the ball on the inner raceway,while radial load predominantly affects the heat generation of the ball due to sliding on the inner and outer raceways.The findings of the work can guide the design and performance evaluation of angular contact ball bearings and provide support for temperature simulation. 展开更多
关键词 Angular contact ball bearings Combined loads Thermodynamic real-time coupling Thermal characteristics
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Experimental study on real-time monitoring of surrounding rock 3D wave velocity structure and failure zone in deep tunnels 认领 引用
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作者 Hongyun Yang Chuandong Jiang +4 位作者 Yong Li Zhi Lin Xiang Wang Yifei Wu Wanlin Feng 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期423-437,共15页
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a... An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering. 展开更多
关键词 Deep-buried tunnel Microseismic monitoring Wave velocity tomography Surrounding rock damage zone Real-time monitoring
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Deep learning powered real-time state of charge monitoring for vanadium flow battery energy storage system 认领 引用
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作者 Tianyu Li Jiawei Sun +8 位作者 Shengkai Xu Xijun Wang Huacheng Wu Feng Xing Zhao Ma He Jiang Zonghao Liu Tao Liu Xianfeng Li 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第7期259-265,I0008,共7页
Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system... Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system management.However,accurate SOC determination during operation remains challenging due to vanadium ion crossover and side reactions that disrupt the valence and concentration balance between positive and negative electrolytes.Herein,an inverted transformer model,namely iTransformer,is employed to predict the SOC in VFB systems during charge–discharge cycles.The iTransformer-SOC model can achieve high accuracy and robustness.Even with training limited to the first three cycles,the model predicts SOC for the next 21 cycles with mean absolute percentage error(MAPE)less than 0.03.It can adapt to power variations and electrolyte rebalancing scenarios.Most importantly,an iTransformer-based SOC monitoring system was validated and confirmed by a 10 kW VFB system,demonstrating superior performance in predicting SOC of complete charge–discharge cycles(MAPE<0.05,less than 1/3 of the traditional open-circuit voltage(OCV)method's error).This datadriven approach provides a robust framework for real-time SOC monitoring in VFB systems,serving as a complementary alternative to physics-based model without requiring prior knowledge of system dynamics. 展开更多
关键词 Vanadium flow battery State of charge Data-driven iTransformer-based SOC model Real-time SOC monitoring
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Real-time early warning of landslide disaster risks on major highways in Ganzhou City,China 认领 引用
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作者 Faming Huang Yang Yang +6 位作者 Guotao Ma Mohammad Rezania Zhilu Chang Filippo Catani Bingchen Jiang Xi Chen Fei Guo 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5218-5241,共24页
Extreme weather events like heavy rainfall have become more frequent recently,increasing the occurrence of landslides and slope instability along mountainous highways and threatening transportation safety.This researc... Extreme weather events like heavy rainfall have become more frequent recently,increasing the occurrence of landslides and slope instability along mountainous highways and threatening transportation safety.This research aims to develop an effective real-time early warning system for highway landslides triggered by extreme weather.Using landslides along Ganzhou's major highways as a case study,a 250-m buffer zone was established along the roads,within which 88,497 slope units were divided using multi-scale segmentation.Subsequently,1547 landslide samples and 18 conditioning factors were collected for landslide susceptibility prediction(LSP)based on random forest(RF),C5.0 decision tree(DT),and support vector machine(SVM)models.Model performance was evaluated using receiver operating characteristic(ROC)curves,the distribution characteristics of the landslide susceptibility index(LSI),and a confusion matrix.A continuous probability rainfall threshold equation was then fittedusing data from the rainfall station.Subsequently,the analytic hierarchy process(AHP)method was employed to assess highway vulnerability.Finally,by integrating LSI,rainfall thresholds,vulnerability,and disaster-bearing entities,effective early warning was achieved for two typical landslide cases.Results indicate that the RF model yielded the best LSP outcomes,with an Raux2 of 0.958,an RMSE of 0.069,and a sum of squared residuals of 0.331 for the continuous probability equation.The hazard assessment reached 90.4%accuracy,with hazard values exceeding 0.8 in both typical cases.AHP analysis,validated by expert experience and consistency tests,identifiedslope,road density,and road grade as key vulnerability factors.Ultimately,real-time risk early warning for typical landslide events was achieved by incorporating population distribution and economic value. 展开更多
关键词 Highway landslide risk Buffer selection Distribution of disaster-bearing entities Real-time early warning
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Real-time decision support for bolter recovery safety:Long short-term memory network-driven aircraft sequencing 认领 引用
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作者 Wei Han Changjiu Li +4 位作者 Xichao Su Yong Zhang Fang Guo Tongtong Yu Xuan Li 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期184-205,共22页
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th... The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations. 展开更多
关键词 Carrier-based aircraft Recovery scheduling Deep reinforcement learning Long short-term memory networks Dynamic real-time decision-making
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Perceiving the Battery Multi-Electrochemical States in Real-time Based on Model-Informed Neural Network 认领 引用
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作者 Ying Zhang Qinghao Zhang +3 位作者 Bin Duan Pingwei Gu Changlong Li Chenghui Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1041-1053,共13页
Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in a... Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in an advanced battery management system.Yet conventional pseudo-two-dimensional(P2D)physics methods suffer from high computational complexity and limit their online application.Thus,we develop a modelinformed neural network(MINN)framework that synergistically combines deep learning with a physics-based model to accurately monitor the battery electrochemical state(such as lithium-ion concentration,plating potential).Firstly,the MINN model is constructed with the innovative loss term containing experimentally measurable parameters and governing physical laws.Secondly,a composite framework based on a convolutional neural network(CNN)architecture is integrated to automatically extract features and enforce spatial boundary conditions,which significantly reduces the number of boundary loss terms that need to be solved and alleviates the complexity of the training process.After training,the MINN model can achieve an accurate estimation of internal states and even their spatiotemporal distributions that cannot be directly measured based on limited observable data and physical laws.At last,by incorporating dynamic current input,the well-trained basic model exhibits strong robustness and can be directly transferred to other cycling protocols with high accuracy,requiring no further retraining.MINN is a novel and promising framework to realize online and accurate micro electrochemical states monitoring,achieving at least 776 times speedup compared with the P2D model.As an innovative artificial intelligence assisted modeling for electrochemical systems,this framework enables root-cause analysis of battery behavior and failure modes,while empowering the management system with more reliable and trustworthy decision-making capabilities. 展开更多
关键词 Electrochemical state estimation lithium-ion battery model-informed neural network(MINN) real-time application
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FragSAM:Near real-time rock fragment segmentation for size distribution analysis across diverse engineering domains 认领 引用
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作者 Yudi Tang Yulin Wang +4 位作者 Jixiong Zhang Runzhe Hu Changwei Wang Joung Oh Guangyao Si 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第6期1167-1188,共22页
Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have b... Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have been increasingly applied to FSD analysis,they are often case-specific,showing limited cross-site generalization despite their accuracy.To address these challenges,FragSAM,an end-to-end,fully automated framework is proposed for near real-time rock fragment segmentation and FSD analysis across diverse engineering environments.FragSAM integrates the generalization power of Segment Anything Model(SAM)with a context-aware prompting mechanism and lightweight architecture for efficient dense fragment segmentation.In Stage 1,an enhanced SAM automatically generates high-quality annotations,which are used to train a modified CenterNet for precise centroid prediction.In Stage 2,these centroids serve as prompts for EdgeSAM,a lightweight SAM variant optimized for real-time inference.This two-stage design eliminates dense grid prompting and reduces reliance on heavy postprocessing,enabling efficient and scalable segmentation.Experimental results show that FragSAM achieves competitive segmentation performance with significantly lower latency and model complexity compared to existing SAM-based methods.In comparison with supervised learning approaches,it also demonstrates superior generalization and performs better in low-quality or unseen scenarios.Furthermore,case studies on blasting fragmentation,TBM muck,and coastal rock surfaces confirm its robustness and seamless cross-site adaptability,requiring no tuning or retraining,making it highly practical for on-site applications. 展开更多
关键词 Segment anything model Rock fragment size distribution FragSAM Prompt-based vision models Near real-time segmentation
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Control strategy for secondary frequency regulation in hybrid energy storage considering real-time dispatchable power and maximum output constraints 认领 引用
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作者 Cuiping Li Liang Zhang +4 位作者 Wenbo Si Caiqi Jia Xingxu Zhu Gangui Yan Junhui Li 《Global Energy Interconnection》 EI CSCD 2026年第3期471-488,共18页
Hybrid energy storage systems(HESSs)involved in secondary frequency regulation(FR)can overcome the technical limitations of single energy storage systems(ESSs).However,coordinating the control of ESSs with differing c... Hybrid energy storage systems(HESSs)involved in secondary frequency regulation(FR)can overcome the technical limitations of single energy storage systems(ESSs).However,coordinating the control of ESSs with differing characteristics remains a major challenge.In this study,we propose a cooperative control strategy for HESSs in automatic generation control FR.First,the maximum output dynamic adjustment factor of the flywheel energy storage system(FESS)and the real-time dispatchable power of ESSs are introduced to constrain the charge/discharge power of ESSs.Subsequently,a coordinated allocation strategy of prioritizing the FESS,i.e.,battery energy storage system(BESS)supplementation,is adopted to pre-allocate the FR power of HESSs between BESSs and FESSs.Second,we minimized the energy loss and balanced the state of charge(SOC)of each ESS to redistribute the pre-allocated FR power of each ESS among the internal energy storage units.Finally,we conducted a simulation analysis using actual operational data.The findings indicate that the proposed strategy can reduce the lifetime loss of the BESS and enhance the continuous operating capability of the FESS.This system can also reduce the energy loss in each ESS,thereby effectively maintaining the SOC equilibrium of each system. 展开更多
关键词 Hybrid energy storage Dynamic adjustment factor Real-time dispatchable power Energy loss SOC balancing
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Tunnel boring machine jamming anticipation based on a new monitoring system for real-time perception of shield strain 认领 引用
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作者 Yucong Pan Quansheng Liu +3 位作者 Xinlin Wu Shaohui Tang Xing Huang Yalong Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5562-5572,共11页
Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a s... Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a series of methods have been proposed to perceive TBM jamming under the condition of soft and fractured surrounding rocks.However,most of these methods cannot predict TBM jamming accurately in advance or perceive the jamming process in real-time.In the present study,a real-time monitoring system(composed of strain gauges,data acquisition,data transmission and data storage)for shield strain was developed and implemented in a TBM at the Lanzhou Water Resource Project in China.The shield strain of the double-shielded TBM was monitored and analysed continuously.The working conditions of the TBM(such as excavation or standstill,jamming or disjamming)were identifiedby analysing the characteristics of shield strain.The perception information was compared to the excavation records.The results indicate that there is good consistency between them.Combined with monitoring information and fieldsurveys,the interaction mechanisms between the shield and surrounding rock were analysed qualitatively.The jamming mechanisms of the two accidents were revealed according to the variation in stable shield strain.A criterion for anticipation TBM jamming was proposed based on the shield strain characteristics(such as trend,amplitude,and magnification).These studies provide references for real-time perception and accurate anticipation of TBM jamming in soft and fractured surrounding rock conditions. 展开更多
关键词 Tunnel boring machine(TBM)jamming Shield strain Real-time perception Anticipation criterion Monitoring system
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Real-Time Optimization of Vertical Roller Mills Using XGBoost Prediction and Q-Learning Control 认领 引用
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作者 Anping Wan Yingchang Gao +2 位作者 Weikang Liu Rui Yin Khalil Al-Bukhaiti 《Computers, Materials & Continua》 SCIE EI 2026年第8期1515-1534,共20页
Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including ex... Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including excessive vibration and main motor current fluctuations)that drive unplanned downtime,increased wear,and reduced throughput.Despite their importance,real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure,feed rate,separator speed,and aerodynamic factors,which limit traditional control strategies under varying loads.This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence(AI).From a 5400 kW Loesche LM56.4 mill,2,764,800 samples were collected at 1 Hz over 32 days of continuous production.A systematic pipeline was developed:quartile-based outlier-robust cleaning;domain-informed feature engineering including Total Current;Random Forest(RF)permutation importance selection of the top 15 parameters;and Extreme Gradient Boosting(XGBoost)regression models with hyperparameters tuned by Tree-structured Parzen Estimator(TPE)Bayesian optimization.The resulting models achieved strong predictive performance,Mean Absolute Percentage Error(MAPE)of 1.3%(95%CI:1.1%–1.5%)for main motor current(R2=0.9997)and 5.8%(95%CI:5.3%–6.3%)for shell vibration(R2=0.9717),representing reductions of 89%and 59%,respectively,relative to the Long Short-Term Memory(LSTM)baseline.These surrogates were embedded into a tabular Q-learning Reinforcement Learning(RL)agent that autonomously adjusts feed rate,grinding pressure,separator speed,and exhaust damper position via a discrete action space and multi-objective reward function,communicating with the Distributed Control System(DCS)via Open Platform Communications Unified Architecture(OPC-UA).Closed-loop evaluation yielded simultaneous reductions of 6.0%in peak current(181.92→170.04 A)and 9.4%in peak vibration(5.51→4.99 mm/s)while maintaining throughput.A PyQt5-based graphical interface enabling real-time monitoring,predictive alerts,and automatic DCS write-back was deployed and operated stably for two weeks. 展开更多
关键词 Vertical roller mill operational optimization XGBoost Bayesian hyperparameter optimization Q-learning energy efficiency vibration reduction real-time control Industry 4.0
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A Single-Device Environment-Adaptive Mixed Reality Framework for Real-Time Industrial Fault Diagnosis 认领 引用
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作者 Xueyi Li Bo Kang +3 位作者 Jing Tang Qi Li Tianyang Wang Minwei Zhang 《Journal of Dynamics, Monitoring and Diagnostics》 2026年第1期64-73,共10页
In industrial environments,monitoring and fault diagnosis of mechanical equipment face challenges such as spatial localization drift and delays in real-time data rendering,especially in complex settings with low illum... In industrial environments,monitoring and fault diagnosis of mechanical equipment face challenges such as spatial localization drift and delays in real-time data rendering,especially in complex settings with low illumination,weak textures,and strong interference.Traditional methods struggle to effectively integrate monitoring data with physical entities,increasing cognitive load and reducing diagnostic accuracy.To address these issues,we propose the Single-Device Mixed Reality(SEMR)framework,a novel solution that enhances industrial equipment monitoring and fault diagnosis.The framework integrates three key mechanisms:an environment-aware model that adjusts the confidence of Simultaneous Localization and Mapping(SLAM)to ensure precise spatial registration,a Kalman filter-based motion prediction to reduce rendering delays,and a faulttolerant gaze interaction system for hands-free operation.Experimental results demonstrate that SEMR reduces the spatial registration error by 52.1%,from 14.2 cm to 6.8 cm,and decreases latency during dynamic inspections by 26.7%,improving diagnostic accuracy and real-time performance.The proposed method provides a costeffective and reliable solution for enhancing industrial fault diagnosis and equipment monitoring,particularly in challenging environments. 展开更多
关键词 adaptive SLAM fault diagnosis industrial inspection predictive interaction real-time equipment status monitoring single-device mixed reality
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An adaptive mesh-based framework for real-time light fluence correction in photoacoustic tomography 认领 引用 被引量：1
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作者 Long Chen Zhaoyong Liang +3 位作者 Chaobin Hu Jinchuan He Anqi Wei Li Qi 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第3期116-131,共16页
Photoacoustic tomography(PAT),which combines excellent optical contrast with high acoustic resolution,has emerged as a promising medical imaging technique.However,achieving rapid,real-time light fluence correction for... Photoacoustic tomography(PAT),which combines excellent optical contrast with high acoustic resolution,has emerged as a promising medical imaging technique.However,achieving rapid,real-time light fluence correction for quantitative PAT imaging remains challenging.To address this,this study introduces an adaptive mesh-based method for the finite element calculation of light fluence to significantly accelerate the correction process for quantitative PAT.Evaluation on a commercial PAT system shows that while achieving comparable imaging quality,the adaptive mesh-based method requires only 1/85 of the mesh points used in a uniform mesh and reduces computation time by more than 20-fold.Our approach effectively enhances the efficiency of light fluence correction,providing strong support for advancing the practical application of photoacoustic imaging technology. 展开更多
关键词 Photoacoustic tomography adaptive mesh real-time light fluence correction
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Real-time FPGA-LabVIEW homebuilt system for noise reduction in AFM imaging 认领 引用 被引量：1
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作者 SUN Qiuyuan YAO Zhifei +8 位作者 SUN Jianheng ZHAO Bohui ZHANG Ketong YAO Haoran WEI Jiuyan WEN Huanfei TANG Jun MA Zongmin LIU Jun 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期355-366,共12页
Atomic force microscopy(AFM)probe vibration monitoring is essential for achieving accurate nanoscale imaging and reliable signal interpretation.This paper presents a low-noise vibration detection method based on a rea... Atomic force microscopy(AFM)probe vibration monitoring is essential for achieving accurate nanoscale imaging and reliable signal interpretation.This paper presents a low-noise vibration detection method based on a real-time FPGA-LabVIEW homebuilt system.The FPGA receives signals from a quadrant photodiode(QPD),performs analog-to-digital conversion and parallel processing,and integrates cascaded digital filters for noise reduction.A finite impulse response(FIR)low-pass filter extracts the static spot position,while an infinite impulse response(IIR)band-pass filter preserves the probe’s resonance vibrations.Compared with conventional analog detection,the proposed system reduces background noise by approximately 50%(measured as 50.23%),enhances the signal-to-noise ratio(SNR)from 15 dB to 20 dB,and maintains FPGA signal-processing latency below 5μs.This work demonstrates that the proposed real-time FPGA-LabVIEW AFM noise optimization system significantly improves signal-to-noise ratio and real-time performance,providing a practical solution for high-precision,low-noise AFM imaging. 展开更多
关键词 atomic force microscopy(AFM) FPGA-LabVIEW homebuilt system real-time processing noise reduction digital filters
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Real-time Estimation of Instantaneous Speed in Diesel Engines Based on the Extended Kalman Filter 认领 引用 被引量：1
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作者 Zhongxin Shi Hongzi Fei +2 位作者 Liuping Wang Bingxin Liu Yilin Liu 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第3期702-712,共11页
The instantaneous speed of diesel engines contains an abundance of information,regarding fuel supply stability and individual cylinder performance.Real-time acquisition of accurate instantaneous speed is crucial for m... The instantaneous speed of diesel engines contains an abundance of information,regarding fuel supply stability and individual cylinder performance.Real-time acquisition of accurate instantaneous speed is crucial for monitoring cylinder-to-cylinder uniformity,diagnosing faults,and enabling precise speed control in marine diesel engines.However,measurement noise distorts the signal,which makes it significantly difficult to monitor the effective information in the actual operation.To address this challenge,this paper proposes a novel real-time filtering method using an extended Kalman filter(EKF).According to the characteristics of crankshaft instantaneous speed of diesel engine,a dedicated state-space model is derived.The EKF utilizes the model to perform real-time feedback and rolling optimization effectively suppressing noise.The performance of the method proposed is validated using both simulated signals and experimental data from a four-cylinder marine diesel engine.Simulation and experimental results demonstrate that the coefficient of determination R2 between the estimated and actual speed reaches 99.83%,while the signal-to-noise ratio(SNR)improves by above 10%on average across different operating conditions.This enhancement enables reliable real-time engine state monitoring and control. 展开更多
关键词 Marine diesel engine Crankshaft instantaneous speed State-space model Extended kalman filter Real-time estimation Noise filtering
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A transfer learning framework for the real-time detection of atmospheric gravity waves from All-Sky Airglow Imager 认领 引用 被引量：1
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作者 YuBin He QingChen Xu +6 位作者 YaJun Zhu QinZeng Li Cui Tu Bing Cai Wei Yuan XinYing Wang Feng Wei 《Earth and Planetary Physics》 EI CSCD 2026年第3期454-462,共9页
Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual s... Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual screening methods and existing machine learning approaches do not meet the demands of practical station monitoring,which has significantly impeded climatological statistical research based on AGWs.Therefore,a real-time detection framework for ground-based airglow gravity waves that integrates transfer learning with adaptive image preprocessing has been proposed.By employing wavelength-adaptive median filtering and multiscale fusion,the framework effectively suppresses stellar noise while preserving weak gravity wave features.The model utilizes an EfficientNet-B3(convolutional neural network)backbone enhanced with a deformable convolutional layer,trained via a two-stage strategy:A frozen phase prevents overfitting by locking the lower level feature extractor,and a fine-tuning phase optimizes the deformable convolution through cosine annealing and layered optimization.This approach improves both feature transfer efficiency and gravity wave detection sensitivity.The resulting lightweight model achieves 91.2%accuracy with millisecond-level inference speed(23 ms per frame). 展开更多
关键词 atmospheric gravity waves All-Sky Airglow Imager real-time detection multiscale fusion transfer learning EfficientNet-B3 deformable convolution
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