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
In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower p...In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower plants.However,it has also brought severe challenges to the operation and maintenance of hydropower plants.Traditional hydropower plant operation and maintenance suffer from problems such as low efficiency,equipment aging,and high labor costs,which seriously hinder the innovation and upgrading of hydropower plant operation and maintenance.Therefore,this article focuses on the operation and maintenance of hydropower plants,summarizes a series of innovative strategies,and applies them in practice to effectively improve the operation and maintenance level of hydropower plants.展开更多
The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This pap...The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This paper presents the design and implementation of an intelligent operation and maintenance(O&M)system that integrates a 3D holographic digital twin cloud platform with UAV-assisted inspection and localized cleaning.The proposed system supports multi-source data acquisition,including UAV imagery,infrared sensing,and DustIQ-based soiling monitoring,and provides real-time visualization of the PV plant through 1:13D reconstruction.UAVs are employed for both autonomous inspections,covering defects such as soiling,bird droppings,bypass diode faults,and panel disconnections and targeted cleaning in small water-covered areas.Field trials were conducted at Riyue and Chebu PV plants,with small-scale UAV cleaning validation in Chebu fish ponds.Results demonstrated that the system achieves efficient task scheduling,fault detection,and localized cleaning,thereby improving O&M efficiency,reducing costs,and enabling digitalized and intelligent management for large-scale PV stations.展开更多
With the continuous development of digital technology,urban management and urban construction have undergone tremendous changes,exerting a profound impact on people’s lives.As a vital component of cities,urban road i...With the continuous development of digital technology,urban management and urban construction have undergone tremendous changes,exerting a profound impact on people’s lives.As a vital component of cities,urban road infrastructure is closely related to the daily lives of citizens.The application of digital twin technology can provide more support for the full lifecycle operation and maintenance management of urban road infrastructure,effectively improving the quality and efficiency of operation and maintenance management,ensuring the effectiveness of urban road infrastructure,and building a higher-quality urban life.Based on urban road infrastructure,this paper analyzes the application value of digital twin technology,proposes strategies for full lifecycle operation and maintenance management,and offers more references for urban construction.展开更多
With the rapid advancement of information technology,Building Information Modeling(BIM)is being increasingly utilized in the construction industry,demonstrating significant potential and value in construction operatio...With the rapid advancement of information technology,Building Information Modeling(BIM)is being increasingly utilized in the construction industry,demonstrating significant potential and value in construction operations and maintenance management.Based on this,this paper conducts an in-depth discussion on BIM technology in building operation and maintenance management,analyzing its challenges and countermeasures,with the aim of providing readers with useful insights and references.展开更多
This paper focuses on electrical fault diagnosis and operation and maintenance technology in property service electromechanical engineering.It details core diagnostic methods,application-oriented tools,predictive main...This paper focuses on electrical fault diagnosis and operation and maintenance technology in property service electromechanical engineering.It details core diagnostic methods,application-oriented tools,predictive maintenance frameworks,and enhanced maintenance planning.It also explores wireless sensor networks,big data analytics,and design-phase applications.Case studies in construction and operation phases are presented.Challenges like legacy system retrofitting are noted,and future potential in quantum sensing and edge AI is discussed.展开更多
This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M...This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M,it proposes O&M strategies such as intelligent fault prediction and diagnosis,intelligent system performance optimization,intelligent system security protection,and adaptive system O&M implementation.Practical applications reveal that AI intelligence technology offers significant advantages in information system O&M,effectively addressing pain points of traditional O&M techniques,such as low fault prediction rates,slow repair speeds,poor security interception,and high labor costs.This substantially enhances the effectiveness of information system O&M.展开更多
This article elaborates on the application of smart water management in the construction of water plants and the operation and maintenance of pipeline networks,covering all layers of the technical framework,including ...This article elaborates on the application of smart water management in the construction of water plants and the operation and maintenance of pipeline networks,covering all layers of the technical framework,including IoT perception.It introduces full chain application scenarios,such as water source monitoring.It also involves BIM,intelligent IoT device applications,breakthroughs in pipeline monitoring system construction and operation technology,as well as the economic cost-effectiveness of innovative models,evaluation index systems,system iteration and upgrading strategies,and points out limitations and future development directions.展开更多
This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dyn...This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dynamics model of sewage treatment facility expansion.Elaborating on detection technology,construction of an intelligent operation and maintenance system,and factors to be considered for sewage plant expansion,it emphasizes the importance of collaborative development and verifies benefits through the PSR model.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based...This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.展开更多
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
Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operato...Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.展开更多
On December 18,2025, Hainan Free Trade Port (Hainan FTP) officially began islandwide special customs operations.Although only two months have passed since this landmark step, the shift is already visible everywhere—f...On December 18,2025, Hainan Free Trade Port (Hainan FTP) officially began islandwide special customs operations.Although only two months have passed since this landmark step, the shift is already visible everywhere—from the bustling flow of international passengers at Haikou Meilan International Airport to the steady stream of cargo vessels calling at Yangpu Port, and even in the sustained attention investors are paying to “Hainan-related” stocks.Together, these signals point to one clear conclusion:China’s largest special economic zone has entered a new phase of development.展开更多
This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plan...This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation,prevent degradation,and avoid loss of efficiency.In this sense,predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities.However,if the sensor fails,there will be an incorrect forecasting of abnormalities.Among the different types of operational faults that sensors can present are drift-related faults,which are probably the most difficult to detect due to a slow but progressive loss of accuracy in measurements.Another problem with predictive maintenance is that it often requires enormous training data,which is not available at the early stage of plant operation.The developed fuzzy system is responsible for detecting faulty readings arising from drift sensor signals,while the neural network complements the function of the fuzzy system by predicting sensor signals when enough training data are available.The AI-based observer and the fuzzy rules are validated in an experimental case study with a 1 Nm^(3)/h electrolyser.The selected variables are electrolyser temperature and efficiency.Experimental results show that the rules of the fuzzy component of the AI-based observer guarantee an accuracy of±0.25 within the range of 0 to 1,and the neural network component predicted correct sensor values with a root mean square error(RMSE)as low as 0.0016.The authors’approach helps to determine drift faults without additional sensors or components installed in the plant.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
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions....Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.展开更多
The large-scale integration of power electronic interface-based renewable energy with intermittency and uncertainty poses severe challenges for power system secure operation,especially frequency security.Determining t...The large-scale integration of power electronic interface-based renewable energy with intermittency and uncertainty poses severe challenges for power system secure operation,especially frequency security.Determining the system frequency regulation ability under contingency is an open problem.To bridge this gap,a unit commitment(UC)to concentrate solar power considering operational risk and frequency dynamic constraints(RFUC-CSP)is proposed in this paper.A concentrating solar power(CSP)plant with renewable energy characteristics and synchronous units is employed to improve renewable energy utilization and provide frequency support.Firstly,an analytical operational risk model is established to quantify the operational risk under renewable energy integration.Then,the frequency dynamic response characteristic of the system is considered to construct frequency security constraints.A novel RFUC-CSP framework is formulated by incorporating operational risk and frequency security constraints into the UC model,which can allocate operational flexibility of power systems by optimizing the admissible uncertainty level to reduce operational risk.The effectiveness of the proposed RFUC-CSP model is demonstrated by case studies on the modified IEEE 30-bus and IEEE RTS-79 system,and the cost-effectiveness of the CSP plant is quantified.展开更多
Loday introduced di-associative algebras and tri-associative algebras motivated by periodicity phenomena in algebraic K-theory.The purpose of this paper is to study the splittings of operations on di-associative algeb...Loday introduced di-associative algebras and tri-associative algebras motivated by periodicity phenomena in algebraic K-theory.The purpose of this paper is to study the splittings of operations on di-associative algebras and tri-associative algebras.We introduce the notion of a quad-dendriform algebra,which is a splitting of a di-associative algebra.We show that a relative averaging operator on dendriform algebras gives rise to a quad-dendriform algebra.Furthermore,we introduce the notion of six-dendriform algebras,which are splittings of the tri-associative algebras,and demonstrate that homomorphic relative averaging operators induce six-dendriform algebras.展开更多
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.
文摘In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower plants.However,it has also brought severe challenges to the operation and maintenance of hydropower plants.Traditional hydropower plant operation and maintenance suffer from problems such as low efficiency,equipment aging,and high labor costs,which seriously hinder the innovation and upgrading of hydropower plant operation and maintenance.Therefore,this article focuses on the operation and maintenance of hydropower plants,summarizes a series of innovative strategies,and applies them in practice to effectively improve the operation and maintenance level of hydropower plants.
基金Joint Innovation Program of Guangdong(Project No.:2023A0505020003)。
文摘The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This paper presents the design and implementation of an intelligent operation and maintenance(O&M)system that integrates a 3D holographic digital twin cloud platform with UAV-assisted inspection and localized cleaning.The proposed system supports multi-source data acquisition,including UAV imagery,infrared sensing,and DustIQ-based soiling monitoring,and provides real-time visualization of the PV plant through 1:13D reconstruction.UAVs are employed for both autonomous inspections,covering defects such as soiling,bird droppings,bypass diode faults,and panel disconnections and targeted cleaning in small water-covered areas.Field trials were conducted at Riyue and Chebu PV plants,with small-scale UAV cleaning validation in Chebu fish ponds.Results demonstrated that the system achieves efficient task scheduling,fault detection,and localized cleaning,thereby improving O&M efficiency,reducing costs,and enabling digitalized and intelligent management for large-scale PV stations.
文摘With the continuous development of digital technology,urban management and urban construction have undergone tremendous changes,exerting a profound impact on people’s lives.As a vital component of cities,urban road infrastructure is closely related to the daily lives of citizens.The application of digital twin technology can provide more support for the full lifecycle operation and maintenance management of urban road infrastructure,effectively improving the quality and efficiency of operation and maintenance management,ensuring the effectiveness of urban road infrastructure,and building a higher-quality urban life.Based on urban road infrastructure,this paper analyzes the application value of digital twin technology,proposes strategies for full lifecycle operation and maintenance management,and offers more references for urban construction.
基金“Research on Operation and Maintenance Management Technology based on BIM and GIS for the Research Basic Ability Improvement Project of Young and Middle-aged Teachers in Universities in Guangxi in 2024--Taking a building in Guilin as an example”(Project No.:2024KY1760)School-level Scientific Research Project of Nanning University of Technology in 2023,“Research on BIM Based Energy Consumption Management Mode of Building Operation and Maintenance”(Project No.:KY202306)。
文摘With the rapid advancement of information technology,Building Information Modeling(BIM)is being increasingly utilized in the construction industry,demonstrating significant potential and value in construction operations and maintenance management.Based on this,this paper conducts an in-depth discussion on BIM technology in building operation and maintenance management,analyzing its challenges and countermeasures,with the aim of providing readers with useful insights and references.
文摘This paper focuses on electrical fault diagnosis and operation and maintenance technology in property service electromechanical engineering.It details core diagnostic methods,application-oriented tools,predictive maintenance frameworks,and enhanced maintenance planning.It also explores wireless sensor networks,big data analytics,and design-phase applications.Case studies in construction and operation phases are presented.Challenges like legacy system retrofitting are noted,and future potential in quantum sensing and edge AI is discussed.
文摘This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M,it proposes O&M strategies such as intelligent fault prediction and diagnosis,intelligent system performance optimization,intelligent system security protection,and adaptive system O&M implementation.Practical applications reveal that AI intelligence technology offers significant advantages in information system O&M,effectively addressing pain points of traditional O&M techniques,such as low fault prediction rates,slow repair speeds,poor security interception,and high labor costs.This substantially enhances the effectiveness of information system O&M.
文摘This article elaborates on the application of smart water management in the construction of water plants and the operation and maintenance of pipeline networks,covering all layers of the technical framework,including IoT perception.It introduces full chain application scenarios,such as water source monitoring.It also involves BIM,intelligent IoT device applications,breakthroughs in pipeline monitoring system construction and operation technology,as well as the economic cost-effectiveness of innovative models,evaluation index systems,system iteration and upgrading strategies,and points out limitations and future development directions.
文摘This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dynamics model of sewage treatment facility expansion.Elaborating on detection technology,construction of an intelligent operation and maintenance system,and factors to be considered for sewage plant expansion,it emphasizes the importance of collaborative development and verifies benefits through the PSR model.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金Supported by the Shenzhen Key Laboratory of Control Theory and Intelligent Systems (ZDSYS20220330161800001)the National Natural Science Foundation of China (62303207)the Guangdong Basic and Applied Basic Research Foundation (2024A1515010725)。
文摘This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.
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support of the“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2024C01028)the State Key Laboratory of Industrial Control Technology,China(ICT2024C04)are gratefully acknowledged.
文摘Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.
文摘On December 18,2025, Hainan Free Trade Port (Hainan FTP) officially began islandwide special customs operations.Although only two months have passed since this landmark step, the shift is already visible everywhere—from the bustling flow of international passengers at Haikou Meilan International Airport to the steady stream of cargo vessels calling at Yangpu Port, and even in the sustained attention investors are paying to “Hainan-related” stocks.Together, these signals point to one clear conclusion:China’s largest special economic zone has entered a new phase of development.
基金support of(1)Grant Ref.PID2023-148456OB-C41 and(2)Grant Ref.RED2022-134588-T found by MICIU/AEI/10.13039/501100011033。
文摘This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation,prevent degradation,and avoid loss of efficiency.In this sense,predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities.However,if the sensor fails,there will be an incorrect forecasting of abnormalities.Among the different types of operational faults that sensors can present are drift-related faults,which are probably the most difficult to detect due to a slow but progressive loss of accuracy in measurements.Another problem with predictive maintenance is that it often requires enormous training data,which is not available at the early stage of plant operation.The developed fuzzy system is responsible for detecting faulty readings arising from drift sensor signals,while the neural network complements the function of the fuzzy system by predicting sensor signals when enough training data are available.The AI-based observer and the fuzzy rules are validated in an experimental case study with a 1 Nm^(3)/h electrolyser.The selected variables are electrolyser temperature and efficiency.Experimental results show that the rules of the fuzzy component of the AI-based observer guarantee an accuracy of±0.25 within the range of 0 to 1,and the neural network component predicted correct sensor values with a root mean square error(RMSE)as low as 0.0016.The authors’approach helps to determine drift faults without additional sensors or components installed in the plant.
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.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
文摘Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.
基金supported by the National Natural Science Foundation of China General Program(No.52277106)the Project funded by China Postdoctoral Science Foundation(No.2022M721773).
文摘The large-scale integration of power electronic interface-based renewable energy with intermittency and uncertainty poses severe challenges for power system secure operation,especially frequency security.Determining the system frequency regulation ability under contingency is an open problem.To bridge this gap,a unit commitment(UC)to concentrate solar power considering operational risk and frequency dynamic constraints(RFUC-CSP)is proposed in this paper.A concentrating solar power(CSP)plant with renewable energy characteristics and synchronous units is employed to improve renewable energy utilization and provide frequency support.Firstly,an analytical operational risk model is established to quantify the operational risk under renewable energy integration.Then,the frequency dynamic response characteristic of the system is considered to construct frequency security constraints.A novel RFUC-CSP framework is formulated by incorporating operational risk and frequency security constraints into the UC model,which can allocate operational flexibility of power systems by optimizing the admissible uncertainty level to reduce operational risk.The effectiveness of the proposed RFUC-CSP model is demonstrated by case studies on the modified IEEE 30-bus and IEEE RTS-79 system,and the cost-effectiveness of the CSP plant is quantified.
基金Supported by the Science and Technology Program of Guizhou Province(Grant No.QKHJC QN[2025]362)the National Natural Science Foundation of China(Grant No.12361005).
文摘Loday introduced di-associative algebras and tri-associative algebras motivated by periodicity phenomena in algebraic K-theory.The purpose of this paper is to study the splittings of operations on di-associative algebras and tri-associative algebras.We introduce the notion of a quad-dendriform algebra,which is a splitting of a di-associative algebra.We show that a relative averaging operator on dendriform algebras gives rise to a quad-dendriform algebra.Furthermore,we introduce the notion of six-dendriform algebras,which are splittings of the tri-associative algebras,and demonstrate that homomorphic relative averaging operators induce six-dendriform algebras.
