Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.展开更多
Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can sig...Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can significantly enhance propulsion efficiency and holds substantial potential for broad applications.However,forming a gas-liquid two-phase flow within the nozzle requires introducing a large amount of rammed seawater.At this time,there is a complex phase transition problem of combustion products in the combustion chamber,which makes the thermodynamic calculation for gas-liquid two-phase water ramjet engines particularly challenging.This paper proposes a thermodynamic calculation method for gas-liquid two-phase water ramjet engines,based on the energy equation for gas-liquid two-phase flow and traditional thermodynamic principles,enabling thermodynamic calculations under conditions of ultra-high water-fuel ratios.Additionally,ground ignition tests of the gas-liquid two-phase engine were conducted,yielding critical engine test parameters.The results demonstrate that the gas-liquid two-phase water ramjet engine achieves a high specific impulse,with a theoretical maximum specific impulse of up to 7000(N s)/kg.The multiphase flow effects significantly impact engine performance,with specific impulse losses reaching up to 25.86%.The error between the thrust and specific impulse in the ground test and the theoretical values is within 10%,validating the proposed thermodynamic calculation method as a reliable reference for further research on gas-liquid two-phase water ramjet engines.展开更多
In permafrost regions of the QinghaiXizang Plateau,embankments of the Qinghai-Xizang Highway and Qinghai-Xizang Railway experiencing roadside water accumulation exhibit more pronounced engineering deteriorations.A wid...In permafrost regions of the QinghaiXizang Plateau,embankments of the Qinghai-Xizang Highway and Qinghai-Xizang Railway experiencing roadside water accumulation exhibit more pronounced engineering deteriorations.A widely accepted view is that the accumulated water adjacent to the embankment possesses substantial thermal energy,which accelerates the degradation-even disappearance-of the underlying permafrost.Moreover,the presence of roadside water keeps the embankment soil in a persistently high-moisture state,thereby making the frozen-soil embankment more susceptible to deformation under traffic loading.However,in the permafrost regions of the QinghaiXizang Plateau,deteriorations of embankments affected by roadside water are more commonly manifested as undulating pavement surfaces,and extensive crack networks appear on the embankment crest even where thermosyphons are installed.These manifestations are not fully consistent with the deterioration mechanisms proposed by existing viewpoints.We propose the hypothesis that temperature gradients,formed due to the freezing and thawing processes between the roadside wateraffected soil and the roadbed soil,lead to moisture migration under the influence of temperature gradients,resulting in frost heave and thaw settlement in the roadbed soil.To validate this hypothesis,we conducted the following investigations sequentially.Initially,we selected a roadbed with a thermosyphon(TPCT)system,which has a significant cooling effect,as the study object.By analyzing the temperature monitoring data of the roadbed section,the temperature variance was calculated to identify the time nodes where the temperature gradient of the roadbed soil was maximum and minimum.Subsequently,corresponding roadbed temperature distribution maps were drawn,illustrating the changes in the temperature and position of the lowtemperature core near the TPCT over time.Furthermore,using small-scale indoor model experiments,we qualitatively concluded that moisture in the soil migrates toward the TPCT due to the temperature gradient.Thereafter,combining borehole water content data and precipitation data from the sloped terrain construction site,the formation mechanisms and timing characteristics of roadside water accumulation were analyzed.Ultimately,by integrating the ground temperature data,air temperature data,roadside water formation mechanisms,and the operating characteristics of the TPCT,it was concluded that roadside water,while in a thawed state during TPCT operation,acts as a supplementary source for moisture migration in the roadbed soil.This migration leads to cracking in the TPCT roadbed.Therefore,this study reveals a novel damage mechanism:asynchronous freeze-thaw processes induce temperature gradients,which drive the migration of roadside water into the roadbed and are responsible for the cracking damage.展开更多
This study investigates the droplet formation for the liquid–liquid two-phase flow within a square T-junction microchannel through numerical simulation using volume of fluid method and experimental visualization usin...This study investigates the droplet formation for the liquid–liquid two-phase flow within a square T-junction microchannel through numerical simulation using volume of fluid method and experimental visualization using high-speed camera imaging.The T-junction microchannel has a cross-sectional width of 0.6 mm and a total length of 27.3 mm.The solution of cyclohexane with 2%and 3%mass concentrations of sorbitan trioleate surfactant were used as the continuous phase,and water was used as the dispersed phase.Slug flow,characteristic of squeezing regime,were predominantly observed.The effects of liquid–liquid two-phase flow rate ratio,and dimensionless number on droplet size,and pressure drop were investigated.The squeezing regime was mapped for 0.0005≤Ca_(c)≤0.0052(capillary number)and 0.1≤q≤10(flow rate ratio).The pressure drops of slugs were in the range from 40 Pa to 200 Pa.The slug lengths were measured between 1 mm and 9 mm.A universal flow map dependent on Ca_(c)Re_(d)^(0.5) are projected to investigate the droplet formation behavior in T-junction microchannel.Correlation expressions are proposed to predict pressure drops and the slug lengths for liquid–liquid two-phase flow in a square T-junction microchannel,using dimensionless numbers such as flow rate ratio and capillary number.The result shows that large continuous phase flow rates facilitate smaller slugs,whereas higher dispersed phase flow rates result in longer shorts.展开更多
In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictiv...In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review cover...Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.展开更多
Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefor...Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefore,real-time monitoring of solid–liquid two-phase flow in pipelines is crucial for system maintenance.This study develops an autoencoder-based deep learning framework to reconstruct three-dimensional solid–liquid two-phase flow within flexible vibrating pipelines utilizing sparse wall information from sensors.Within this framework,separate X-model and F-model with distinct hidden-layer structures are established to reconstruct the coordinates and flow field information on the computational domain grid of the pipeline under traveling wave vibration.Following hyperparameter optimization,the models achieved high reconstruction accuracy,demonstrating R^(2)values of 0.990 and 0.945,respectively.The models’robustness is evaluated across three aspects:vibration parameters,physical fields,and vibration modes,demonstrating good reconstruction performance.Results concerning sensors show that 20 sensors(0.06%of total grids)achieve a balance between accuracy and cost,with superior accuracy obtained when arranged along the full length of the pipe compared to a dense arrangement at the front end.The models exhibited a signal-to-noise ratio tolerance of approximately 27 dB,with reconstruction accuracy being more affected by sensor failures at both ends of the pipeline.展开更多
This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in ...This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.展开更多
The influence of the squeeze film between the tube and the support structure on flow-induced vibrations is a critical factor in tube bundles subjected to two-phase cross-flow.This aspect can significantly alter the th...The influence of the squeeze film between the tube and the support structure on flow-induced vibrations is a critical factor in tube bundles subjected to two-phase cross-flow.This aspect can significantly alter the threshold for fluidelastic instability and affect heat transfer efficiency.This paper presents a mathematical model incorporating the squeeze film force between the tube and the support structure.We aim to clarify the mechanisms underlying fluidelastic instability in tube bundle systems exposed to two-phase flow.Using a self-developed computer program,we performed numerical calculations to examine the influence of the squeeze film on the threshold of fluidelastic instability in the tube bundle system.Furthermore,we analyzed how the thickness and length of the squeeze film affect both the underlying mechanisms and the critical velocity of fluidelastic instability.展开更多
As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined fact...As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.展开更多
BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receivi...BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receiving maintenance hemodia-lysis(MHD)is limited.AIM To investigate the correlation between anxiety,depression,SPB,and PR in pati-ents with CRF on MHD.METHODS This study included 225 patients with CRF on MHD who were admitted between June 2021 and June 2024.The anxiety level was evaluated using the Self-Rating Anxiety Scale(SAS);the depression status was assessed using the Self-Rating Depression Scale(SDS);the SPB was measured using the SPB Scale(SPBS);and the PR was determined using the Connor–Davidson Resilience Scale(CD-RISC).The correlations among the SAS,SDS,SPB,and CD-RISC were analyzed using Pearson’s correlation coefficients.Univariate and multivariate analyses were performed to identify the factors that influence the PR of patients with CRF on MHD.RESULTS The SAS,SDS,SPB,and CD-RISC scores of the 225 patients were 45.25±15.36,54.81±14.68,32.31±11.52,and 66.48±9.18,respectively.Significant negative correlations were observed between SAS,SDS,SPB,and CD-RISC.Furthermore,longer dialysis vintage(P=0.015),the absence of religious beliefs(P=0.020),lower monthly income(P=0.008),higher SAS score(P=0.013),and higher SDS score(P=0.006)were all independent factors that adversely affected the PR of patients with CRF on MHD.CONCLUSION Patients with CRF on MHD present with varying degrees of anxiety,depression,and SPB,all of which exhibit a significant negative correlation with their PR.Moreover,longer dialysis vintage,the absence of religious beliefs,lower monthly income,higher SAS score,and higher SDS score were factors that negatively affected the PR of patients with CRF on MHD.展开更多
Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristic...Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.展开更多
This work investigated the dynamic behavior of vertical pipes conveying gas-liquid two-phase flow when subjected to external excitations at both ends.Even with minimal excitation amplitude,resonance can occur when the...This work investigated the dynamic behavior of vertical pipes conveying gas-liquid two-phase flow when subjected to external excitations at both ends.Even with minimal excitation amplitude,resonance can occur when the excitation frequency aligns with the natural frequency of the pipe,significantly increasing the degree of operational risk.The governing equation of motion based on the Euler-Bernoulli beam is derived for the relative deflection with stationary simply supported ends,with the effects of the external excitations represented by source terms distributed along the pipe length.The fourth-order partial differential equation is solved via the generalized integral transform technique(GITT),with the solution successfully verified via comparison with results in the literature.A comprehensive analysis of the vibration phenomena and changes in the motion state of the pipe is conducted for three classes of external excitation conditions:same frequency and amplitude(SFSA),same frequency but different amplitudes(SFDA),and different frequencies and amplitudes(DFDA).The numerical results show that with increasing gas volume fraction,the position corresponding to the maximum vibration displacement shifts upward.Compared with conditions without external excitation,the vibration displacement of the pipe conveying two-phase flow under external excitation increases significantly.The frequency of external excitation has a significant effect on the dynamic behavior of a pipe conveying two-phase flow.展开更多
Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer...Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.展开更多
While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect ...While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.展开更多
This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical te...This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.展开更多
Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance ca...Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.展开更多
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adaptin...Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.展开更多
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
基金supported by the Stable Support Fund forBasic Disciplines,China(No.3072024WD0201)。
文摘Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can significantly enhance propulsion efficiency and holds substantial potential for broad applications.However,forming a gas-liquid two-phase flow within the nozzle requires introducing a large amount of rammed seawater.At this time,there is a complex phase transition problem of combustion products in the combustion chamber,which makes the thermodynamic calculation for gas-liquid two-phase water ramjet engines particularly challenging.This paper proposes a thermodynamic calculation method for gas-liquid two-phase water ramjet engines,based on the energy equation for gas-liquid two-phase flow and traditional thermodynamic principles,enabling thermodynamic calculations under conditions of ultra-high water-fuel ratios.Additionally,ground ignition tests of the gas-liquid two-phase engine were conducted,yielding critical engine test parameters.The results demonstrate that the gas-liquid two-phase water ramjet engine achieves a high specific impulse,with a theoretical maximum specific impulse of up to 7000(N s)/kg.The multiphase flow effects significantly impact engine performance,with specific impulse losses reaching up to 25.86%.The error between the thrust and specific impulse in the ground test and the theoretical values is within 10%,validating the proposed thermodynamic calculation method as a reliable reference for further research on gas-liquid two-phase water ramjet engines.
基金supported by the Major Science and Technology Project of Gansu Province(Grant No.24ZD13FA003 and 23ZDWA005)National Natural Science Foundation of China(Grant No.42371140,42301163,41971087 and 42272332)the program of the State Key Laboratory of Cryospheric Science and Frozen Soil Engineering,CAS(No.CSFSEZZ-2411)。
文摘In permafrost regions of the QinghaiXizang Plateau,embankments of the Qinghai-Xizang Highway and Qinghai-Xizang Railway experiencing roadside water accumulation exhibit more pronounced engineering deteriorations.A widely accepted view is that the accumulated water adjacent to the embankment possesses substantial thermal energy,which accelerates the degradation-even disappearance-of the underlying permafrost.Moreover,the presence of roadside water keeps the embankment soil in a persistently high-moisture state,thereby making the frozen-soil embankment more susceptible to deformation under traffic loading.However,in the permafrost regions of the QinghaiXizang Plateau,deteriorations of embankments affected by roadside water are more commonly manifested as undulating pavement surfaces,and extensive crack networks appear on the embankment crest even where thermosyphons are installed.These manifestations are not fully consistent with the deterioration mechanisms proposed by existing viewpoints.We propose the hypothesis that temperature gradients,formed due to the freezing and thawing processes between the roadside wateraffected soil and the roadbed soil,lead to moisture migration under the influence of temperature gradients,resulting in frost heave and thaw settlement in the roadbed soil.To validate this hypothesis,we conducted the following investigations sequentially.Initially,we selected a roadbed with a thermosyphon(TPCT)system,which has a significant cooling effect,as the study object.By analyzing the temperature monitoring data of the roadbed section,the temperature variance was calculated to identify the time nodes where the temperature gradient of the roadbed soil was maximum and minimum.Subsequently,corresponding roadbed temperature distribution maps were drawn,illustrating the changes in the temperature and position of the lowtemperature core near the TPCT over time.Furthermore,using small-scale indoor model experiments,we qualitatively concluded that moisture in the soil migrates toward the TPCT due to the temperature gradient.Thereafter,combining borehole water content data and precipitation data from the sloped terrain construction site,the formation mechanisms and timing characteristics of roadside water accumulation were analyzed.Ultimately,by integrating the ground temperature data,air temperature data,roadside water formation mechanisms,and the operating characteristics of the TPCT,it was concluded that roadside water,while in a thawed state during TPCT operation,acts as a supplementary source for moisture migration in the roadbed soil.This migration leads to cracking in the TPCT roadbed.Therefore,this study reveals a novel damage mechanism:asynchronous freeze-thaw processes induce temperature gradients,which drive the migration of roadside water into the roadbed and are responsible for the cracking damage.
基金supports for this project from the National Natural Science Foundation of China(22378295).
文摘This study investigates the droplet formation for the liquid–liquid two-phase flow within a square T-junction microchannel through numerical simulation using volume of fluid method and experimental visualization using high-speed camera imaging.The T-junction microchannel has a cross-sectional width of 0.6 mm and a total length of 27.3 mm.The solution of cyclohexane with 2%and 3%mass concentrations of sorbitan trioleate surfactant were used as the continuous phase,and water was used as the dispersed phase.Slug flow,characteristic of squeezing regime,were predominantly observed.The effects of liquid–liquid two-phase flow rate ratio,and dimensionless number on droplet size,and pressure drop were investigated.The squeezing regime was mapped for 0.0005≤Ca_(c)≤0.0052(capillary number)and 0.1≤q≤10(flow rate ratio).The pressure drops of slugs were in the range from 40 Pa to 200 Pa.The slug lengths were measured between 1 mm and 9 mm.A universal flow map dependent on Ca_(c)Re_(d)^(0.5) are projected to investigate the droplet formation behavior in T-junction microchannel.Correlation expressions are proposed to predict pressure drops and the slug lengths for liquid–liquid two-phase flow in a square T-junction microchannel,using dimensionless numbers such as flow rate ratio and capillary number.The result shows that large continuous phase flow rates facilitate smaller slugs,whereas higher dispersed phase flow rates result in longer shorts.
基金supported by the National Natural Science Foundation of China(No.51767017)the Key Research and Development Program of Gansu Province(No.25YFGA032)the Industry Support and Guidance Project for Higher Education Institutions of Gansu Province(No.2022CYZC-22).
文摘In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金supported by Ministry of Education of Singapore,under Academic Research Fund Tier 1(Grant Number RG143/23).
文摘Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.
基金financial support by the National Natural Science Foundation of China (Nos.52471293 and 12372270)the National Youth Science Foundation of China (Nos.52101322 and 52108375)+3 种基金the Program for Intergovernmental International S&T Cooperation Projects of Shanghai Municipality, China (Nos.24510711100 and 22160710200)The Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (No.SL2022PT101)funded by the Open Fund of the State Key Laboratory of Coastal and Offshore Engineering of Dalian University of Technology (No.LP2415)National Key R&D Program of China (No.2023YFC2811600)
文摘Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefore,real-time monitoring of solid–liquid two-phase flow in pipelines is crucial for system maintenance.This study develops an autoencoder-based deep learning framework to reconstruct three-dimensional solid–liquid two-phase flow within flexible vibrating pipelines utilizing sparse wall information from sensors.Within this framework,separate X-model and F-model with distinct hidden-layer structures are established to reconstruct the coordinates and flow field information on the computational domain grid of the pipeline under traveling wave vibration.Following hyperparameter optimization,the models achieved high reconstruction accuracy,demonstrating R^(2)values of 0.990 and 0.945,respectively.The models’robustness is evaluated across three aspects:vibration parameters,physical fields,and vibration modes,demonstrating good reconstruction performance.Results concerning sensors show that 20 sensors(0.06%of total grids)achieve a balance between accuracy and cost,with superior accuracy obtained when arranged along the full length of the pipe compared to a dense arrangement at the front end.The models exhibited a signal-to-noise ratio tolerance of approximately 27 dB,with reconstruction accuracy being more affected by sensor failures at both ends of the pipeline.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.
基金financially supported by the National Natural Science Foundation of China(Grant No.12072336).
文摘The influence of the squeeze film between the tube and the support structure on flow-induced vibrations is a critical factor in tube bundles subjected to two-phase cross-flow.This aspect can significantly alter the threshold for fluidelastic instability and affect heat transfer efficiency.This paper presents a mathematical model incorporating the squeeze film force between the tube and the support structure.We aim to clarify the mechanisms underlying fluidelastic instability in tube bundle systems exposed to two-phase flow.Using a self-developed computer program,we performed numerical calculations to examine the influence of the squeeze film on the threshold of fluidelastic instability in the tube bundle system.Furthermore,we analyzed how the thickness and length of the squeeze film affect both the underlying mechanisms and the critical velocity of fluidelastic instability.
基金financially supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant Nos.2023S007 and 2023S165)the Key Research and Development Program of Zhejiang(Grant No.2023C03183).
文摘As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.
基金Supported by Key Research Fund of Wannan Medical College,No.WK2021ZF15Research Foundation for Advanced Talents of Wannan Medical College,No.YR202213+3 种基金Foundation of Anhui Educational Committee,No.2023AH051759Excellent Youth Research Project of Anhui UniversitiesNo.2023AH030107Horizontal Project of Wannan Medical College,No.622202504003 and No.662202404013.
文摘BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receiving maintenance hemodia-lysis(MHD)is limited.AIM To investigate the correlation between anxiety,depression,SPB,and PR in pati-ents with CRF on MHD.METHODS This study included 225 patients with CRF on MHD who were admitted between June 2021 and June 2024.The anxiety level was evaluated using the Self-Rating Anxiety Scale(SAS);the depression status was assessed using the Self-Rating Depression Scale(SDS);the SPB was measured using the SPB Scale(SPBS);and the PR was determined using the Connor–Davidson Resilience Scale(CD-RISC).The correlations among the SAS,SDS,SPB,and CD-RISC were analyzed using Pearson’s correlation coefficients.Univariate and multivariate analyses were performed to identify the factors that influence the PR of patients with CRF on MHD.RESULTS The SAS,SDS,SPB,and CD-RISC scores of the 225 patients were 45.25±15.36,54.81±14.68,32.31±11.52,and 66.48±9.18,respectively.Significant negative correlations were observed between SAS,SDS,SPB,and CD-RISC.Furthermore,longer dialysis vintage(P=0.015),the absence of religious beliefs(P=0.020),lower monthly income(P=0.008),higher SAS score(P=0.013),and higher SDS score(P=0.006)were all independent factors that adversely affected the PR of patients with CRF on MHD.CONCLUSION Patients with CRF on MHD present with varying degrees of anxiety,depression,and SPB,all of which exhibit a significant negative correlation with their PR.Moreover,longer dialysis vintage,the absence of religious beliefs,lower monthly income,higher SAS score,and higher SDS score were factors that negatively affected the PR of patients with CRF on MHD.
基金the National Natural Science Foundation of China (Nos. 42302143, 42172159)China Geological Survey Project (No. DD20211350)support from the G. Albert Shoemaker endowment
文摘Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.
基金financially supported by the Key Research and Development Program of Shandong Province(Grant Nos.2022CXGC020405,2023CXGC010415 and 2025TSGCCZZB0238)the National Natural Science Foundation of China(Grant No.52171288)the financial support from CNPq,FAPERJ,ANP,Embrapii,and China National Petroleum Corporation(CNPC).
文摘This work investigated the dynamic behavior of vertical pipes conveying gas-liquid two-phase flow when subjected to external excitations at both ends.Even with minimal excitation amplitude,resonance can occur when the excitation frequency aligns with the natural frequency of the pipe,significantly increasing the degree of operational risk.The governing equation of motion based on the Euler-Bernoulli beam is derived for the relative deflection with stationary simply supported ends,with the effects of the external excitations represented by source terms distributed along the pipe length.The fourth-order partial differential equation is solved via the generalized integral transform technique(GITT),with the solution successfully verified via comparison with results in the literature.A comprehensive analysis of the vibration phenomena and changes in the motion state of the pipe is conducted for three classes of external excitation conditions:same frequency and amplitude(SFSA),same frequency but different amplitudes(SFDA),and different frequencies and amplitudes(DFDA).The numerical results show that with increasing gas volume fraction,the position corresponding to the maximum vibration displacement shifts upward.Compared with conditions without external excitation,the vibration displacement of the pipe conveying two-phase flow under external excitation increases significantly.The frequency of external excitation has a significant effect on the dynamic behavior of a pipe conveying two-phase flow.
基金supported by the National Natural Science Foundation of China(52104049)the Young Elite Scientist Sponsorship Program by Beijing Association for Science and Technology(BYESS2023262)。
文摘Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.
文摘While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.
文摘This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.
文摘Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.
文摘Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.
