The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train.A fault diagnostic model of on-board equipment is built utilizing the integrated learning X...The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train.A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost(eXtreme Gradient Boosting)algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly.The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms.To begin,the text features were extracted using the improved TF-IDF(Term Frequency-Inverse Document Frequency)approach,and 24 fault feature words were chosen and converted into weight word vectors.Secondly,considering the imbalanced fault categories in the data set,the ADASYN(Adaptive Synthetic sampling)adaptive synthetically oversampling technique was used to synthesize a few category fault samples.Finally,the data samples were split into training and test sets based on the fault text data of CTCS-3train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel.The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search.Compared with other methods,the evaluation index of the XGBoost model was significantly improved.The diagnostic accuracy reached 95.43%,which verifies the effectiveness of the method in text fault diagnosis.展开更多
The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology pro...The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.展开更多
The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is ...The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units,and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts,leading to variations in the dynamic response of the foundation.The high-frequency units yield significantly diverse outcomes under different startup conditions and times,resulting in failure to meet operational requirements,influencing the normal function of the tunnel,and causing harm to the foundation structure,personnel,and property in severe cases.This article formulates a finite element numerical computation model for solid elements using three-dimensional elastic body theory and integrates field measurements to substantiate and ascertain the crucial parameter configurations of the finite element model.By proposing a comprehensive startup timing function for high-frequency dynamic machines under different startup conditions,simulating the frequency andmagnitude variations during the startup process,and suggesting functions for changes in frequency and magnitude,a simulated startup schedule function for high-frequency machines is created through coupling.Taking into account the selection of the transient dynamic analysis step length,the dynamic response results for the lower dynamic foundation during its fundamental frequency crossing process are obtained.The validation checks if the structural magnitude surpasses the safety threshold during the critical phase of unit startup traversing the structural resonance region.The design recommendations for high-frequency units’dynamic foundations are provided,taking into account the startup process of the machine and ensuring the safe operation of the tunnel.展开更多
Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in s...Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.展开更多
Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test b...Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.展开更多
Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-cri...Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.展开更多
Seismic isolation effectively reduces seismic demands on building structures by isolating the superstructure from ground vibrations during earthquakes.However,isolation strategies give less attention to acceleration-s...Seismic isolation effectively reduces seismic demands on building structures by isolating the superstructure from ground vibrations during earthquakes.However,isolation strategies give less attention to acceleration-sensitive systems or equipment.Meanwhile,as the isolation layer’s displacement grows,the stiffness and frequency of traditional rolling and sliding isolation bearings increases,potentially causing self-centering and resonance concerns.As a result,a new conical pendulum bearing has been selected for acceleration-sensitive equipment to increase self-centering capacity,and additional viscous dampers are incorporated to enhance system damping.Moreover,the theoretical formula for conical pendulum bearings is supplied to analyze the device’s dynamic parameters,and shake table experiments are used to determine the proposed device’s isolation efficiency under various conditions.According to the test results,the newly proposed devices have remarkable isolation performance in terms of minimizing both acceleration and displacement responses.Finally,a numerical model of the isolation system is provided for further research,and the accuracy is demonstrated by the aforementioned experiments.展开更多
CJ Health Technology Co.has been an export-oriented supplier of fitness equipment,boasting leading technology innovation and manufacturing capabilities.However,in the process of expanding into the domestic market,the ...CJ Health Technology Co.has been an export-oriented supplier of fitness equipment,boasting leading technology innovation and manufacturing capabilities.However,in the process of expanding into the domestic market,the challenges of unclear market positioning and lack of brand awareness have emerged as major obstacles.To address these issues,a SWOT analysis was conducted to explore a suitable market positioning strategy for the CJ brand.The analysis reveals that the market demand for commercial fitness equipment is relatively saturated,with fierce competition.In contrast,the home fitness market represents a new blue ocean with promising development opportunities for the CJ brand.Leveraging excellent product quality,stringent quality control measures,and innovation-driven cutting-edge technology,it is recommended that CJ brand fitness equipment target young customers aged between 25 and 35 years old.Additionally,vigorously developing the home fitness product line is proposed to capitalize on this burgeoning market segment.展开更多
The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train ...The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based onboard logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.展开更多
With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce ...With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.展开更多
In the past few decades,microbubble flotation has been widely studied in the separation and beneficiation of fine minerals.Compared with conventional flotation,microbubble flotation has obvious advantages,such as high...In the past few decades,microbubble flotation has been widely studied in the separation and beneficiation of fine minerals.Compared with conventional flotation,microbubble flotation has obvious advantages,such as high grade and recovery and low consumption of flotation reagents.This work systematically reviews the latest advances and research progress in the flotation of fine mineral particles by microbubbles.In general,microbubbles have small bubble size,large specific surface area,high surface energy,and good selectivity and can also easily be attached to the surface of hydrophobic particles or large bubbles,greatly reducing the detaching probability of particles from bubbles.Microbubbles can be prepared by pressurized aeration and dissolved air,electrolysis,ultrasonic cavitation,photocatalysis,solvent exchange,temperature difference method(TDM),and Venturi tube and membrane method.Correspondingly,equipment for fine-particle flotation is categorized as microbubble release flotation machine,centrifugal flotation column,packed flotation column,and magnetic flotation machine.In practice,microbubble flotation has been widely studied in the beneficiation of ultrafine coals,metallic minerals,and nonmetallic minerals and exhibited superiority over conventional flotation machines.Mechanisms underpinning the promotion of fine-particle flotation by nanobubbles include the agglomeration of fine particles,high stability of nanobubbles in aqueous solutions,and enhancement of particle hydrophobicity and flotation dynamics.展开更多
Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of...Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.展开更多
The world is currently undergoing profound changes which have never happened within the past century.Global competition in the technology and industry fields is becoming increasingly fierce.The strategic competition o...The world is currently undergoing profound changes which have never happened within the past century.Global competition in the technology and industry fields is becoming increasingly fierce.The strategic competition of the major powers further focuses on the manufacturing industry.Developed countries such as the United States,Germany,and Japan have successively put forward strategic plans such as“re-industrialization”and“return of manufacturing industry”,aiming to seize the commanding heights of a new round of global high-end technology competition and expand international market share.Standing at the historic intersection of a new round of scientific and technological revolution and China's accelerated high-quality development,the“14th Five-Year Plan”clearly pointed out that intelligent manufacturing is the main development trend to promote China's manufacturing to the medium-high end of the global value chain.This reflects the importance of advanced manufacturing for national strategic layout.To better grasp the development direction of advanced manufacturing equipment,the development process and current application status of manufacturing equipment are summarized,and thereafter the characteristics of manufacturing equipment in different development stages of the manufacturing industry are analyzed.Finally,the development trend of advanced milling equipment is prospected.展开更多
As the number of power terminals continues to increase and their usage becomes more widespread,the security of power systems is under great threat.In response to the lack of effective trust evaluation methods for term...As the number of power terminals continues to increase and their usage becomes more widespread,the security of power systems is under great threat.In response to the lack of effective trust evaluation methods for terminals,we propose a trust evaluation model based on equipment portraits for power terminals.First,we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic.Second,we propose an exception evaluation method based on syntax and semantics.The key fields of each message are extracted,and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal.Thus,by combining the network flow order,syntax,and semantic analysis,an equipment portrait can be constructed to guarantee security of the power network terminals.We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time.Finally,the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate,as well as a higher real-time performance,which is more suitable for power terminals.展开更多
To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the att...To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.展开更多
Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis wi...Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.展开更多
Given the existing integrated scheduling algorithms,all processes are ordered and scheduled overall,and these algorithms ignore the influence of the vertical and horizontal characteristics of the product process tree ...Given the existing integrated scheduling algorithms,all processes are ordered and scheduled overall,and these algorithms ignore the influence of the vertical and horizontal characteristics of the product process tree on the product scheduling effect.This paper presents an integrated scheduling algorithm for the same equipment process sequencing based on the Root-Subtree horizontal and vertical pre-scheduling to solve the above problem.Firstly,the tree decomposition method is used to extract the root node to split the process tree into several Root-Subtrees,and the Root-Subtree priority is set from large to small through the optimal completion time of vertical and horizontal pre-scheduling.All Root-Subtree processes on the same equipment are sorted into the stack according to the equipment process pre-start time,and the stack-top processes are combined with the schedulable process set to schedule and dispatch the stack.The start processing time of each process is determined according to the dynamic start processing time strategy of the equipment process,to complete the fusion operation of the Root-Subtree processes under the constraints of the vertical process tree and the horizontal equipment.Then,the root node is retrieved to form a substantial scheduling scheme,which realizes scheduling optimization by mining the vertical and horizontal characteristics of the process tree.Verification by examples shows that,compared with the traditional integrated scheduling algorithms that sort the scheduling processes as an overall,the integrated scheduling algorithmin this paper is better.The proposed algorithmenhances the process scheduling compactness,reduces the length of the idle time of the processing equipment,and optimizes the production scheduling target,which is of universal significance to solve the integrated scheduling problem.展开更多
The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution...The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.展开更多
The current space launch missions are intense, and the utilization of equipment is frequent, demanding increasingly higher responsiveness and capability in maintenance and support. The aerospace equipment maintenance ...The current space launch missions are intense, and the utilization of equipment is frequent, demanding increasingly higher responsiveness and capability in maintenance and support. The aerospace equipment maintenance and support chain relies on aerospace equipment maintenance and support facilities, deploying various maintenance and support resources rationally according to specific requirements and principles, ultimately forming a unidirectional functional chain or network from the supply side to the demand side. This system helps address the “bottleneck” issue in the generation of aerospace equipment support capability and significantly improves the level of aerospace equipment maintenance and support. The model construction is a prerequisite for analyzing the formation and operation mechanism of the chain, and identifying factors affecting the efficiency and effectiveness of maintenance and support. With consideration of the particularity of aerospace equipment maintenance and support, the paper extensively investigates the construction of the aerospace equipment maintenance and support chain model by drawing on research achievements in modern supply chain and logistics theories, as well as model construction methods. It develops a structural diagram-based chain model, with symbols as key elements, and establishes an evaluation indicator system, providing insights into understanding and grasping the composition of the aerospace equipment maintenance and support chain effectively. Furthermore, it offers a reference for solving other equipment support chains’ construction and optimization problems.展开更多
Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect d...Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect detection.At the same time,different types of intelligent sensors can mon-itor environmental information,such as temperature,humidity,dust,etc.Therefore,we apply the Internet of Things(IoT)technology to monitor the related environment and pervasive interconnections to diverse physical objects.However,the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems.Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph,which can be used to organize,share,and analyze equipment defect-related knowledge.At present,there are a lot of relevant data in the field of intelligent equipment defects.These equipment defect data often focus on a single aspect of the defect field.It is difficult to integrate the database with various types of equipment defect information.This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology.Based on ontology,this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities.This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field.The final experiment proves that this model is superior to other models in precision,recall,and F1 value.This research can break the barrier of multi-source heterogeneous knowledge,build an efficient storage engine for multimodal data,and empower the safety of Industrial applications,data,and platforms in multi-clouds for Internet of Things.展开更多
基金supported by the Science and Tec hnology Research and Development Plan Contract of China National Railway Group Co.,Ltd(Grant No.N2022G012)the Railway Science and Technology Research and Development Center Project(Project No.SYF2022SJ004).
文摘The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train.A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost(eXtreme Gradient Boosting)algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly.The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms.To begin,the text features were extracted using the improved TF-IDF(Term Frequency-Inverse Document Frequency)approach,and 24 fault feature words were chosen and converted into weight word vectors.Secondly,considering the imbalanced fault categories in the data set,the ADASYN(Adaptive Synthetic sampling)adaptive synthetically oversampling technique was used to synthesize a few category fault samples.Finally,the data samples were split into training and test sets based on the fault text data of CTCS-3train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel.The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search.Compared with other methods,the evaluation index of the XGBoost model was significantly improved.The diagnostic accuracy reached 95.43%,which verifies the effectiveness of the method in text fault diagnosis.
基金Jilin Science and Technology Development Plan Project(No.20200403075SF)Doctoral Research Start-Up Fund of Northeast Electric Power University(No.BSJXM-2018202).
文摘The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.
基金Smart Integration Key Technologies and Application Demonstrations of Large Scale Underground Space Disaster Prevention and Reduction in Guangzhou International Financial City([2021]–KJ058).
文摘The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units,and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts,leading to variations in the dynamic response of the foundation.The high-frequency units yield significantly diverse outcomes under different startup conditions and times,resulting in failure to meet operational requirements,influencing the normal function of the tunnel,and causing harm to the foundation structure,personnel,and property in severe cases.This article formulates a finite element numerical computation model for solid elements using three-dimensional elastic body theory and integrates field measurements to substantiate and ascertain the crucial parameter configurations of the finite element model.By proposing a comprehensive startup timing function for high-frequency dynamic machines under different startup conditions,simulating the frequency andmagnitude variations during the startup process,and suggesting functions for changes in frequency and magnitude,a simulated startup schedule function for high-frequency machines is created through coupling.Taking into account the selection of the transient dynamic analysis step length,the dynamic response results for the lower dynamic foundation during its fundamental frequency crossing process are obtained.The validation checks if the structural magnitude surpasses the safety threshold during the critical phase of unit startup traversing the structural resonance region.The design recommendations for high-frequency units’dynamic foundations are provided,taking into account the startup process of the machine and ensuring the safe operation of the tunnel.
基金supported by the National Key Research and Development Program of China(No.2023YFB2604600).
文摘Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.
基金The work described here has been supported by the TRANSIT project(funded by EU Horizon 2020 and the Europe’s Rail Joint Undertaking under grant agreement 881771).
文摘Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.
基金the Natural Sciences and Engineering Research Council of Canada(NSERC)under GR012389.
文摘Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.
基金Scientific Research Fund of Institute of Engineering Mechanics,CEA under Grant No.2019A03Scientific Research Fund of Institute of Engineering Mechanics,CEA under Grant No.2021D12National Key R&D Program of China under No.2018YFC1504404。
文摘Seismic isolation effectively reduces seismic demands on building structures by isolating the superstructure from ground vibrations during earthquakes.However,isolation strategies give less attention to acceleration-sensitive systems or equipment.Meanwhile,as the isolation layer’s displacement grows,the stiffness and frequency of traditional rolling and sliding isolation bearings increases,potentially causing self-centering and resonance concerns.As a result,a new conical pendulum bearing has been selected for acceleration-sensitive equipment to increase self-centering capacity,and additional viscous dampers are incorporated to enhance system damping.Moreover,the theoretical formula for conical pendulum bearings is supplied to analyze the device’s dynamic parameters,and shake table experiments are used to determine the proposed device’s isolation efficiency under various conditions.According to the test results,the newly proposed devices have remarkable isolation performance in terms of minimizing both acceleration and displacement responses.Finally,a numerical model of the isolation system is provided for further research,and the accuracy is demonstrated by the aforementioned experiments.
文摘CJ Health Technology Co.has been an export-oriented supplier of fitness equipment,boasting leading technology innovation and manufacturing capabilities.However,in the process of expanding into the domestic market,the challenges of unclear market positioning and lack of brand awareness have emerged as major obstacles.To address these issues,a SWOT analysis was conducted to explore a suitable market positioning strategy for the CJ brand.The analysis reveals that the market demand for commercial fitness equipment is relatively saturated,with fierce competition.In contrast,the home fitness market represents a new blue ocean with promising development opportunities for the CJ brand.Leveraging excellent product quality,stringent quality control measures,and innovation-driven cutting-edge technology,it is recommended that CJ brand fitness equipment target young customers aged between 25 and 35 years old.Additionally,vigorously developing the home fitness product line is proposed to capitalize on this burgeoning market segment.
基金supported by National Natural Science Foundation of China(No.61763025)Gansu Science and Technology Program Project(No.18JR3RA104)+1 种基金Industrial support program for colleges and universities in Gansu Province(No.2020C-19)Lanzhou Science and Technology Project(No.2019-4-49)。
文摘The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based onboard logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(Projects No.52202012)the National Natural Science Foundation of China(Projects No.51834007)。
文摘With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.
基金funded by the National Natural Science Foundation of China (No.52004020)Fundamental Research Funds for the Central Universities (No.00007733)+2 种基金Open Foundation of State Key Laboratory of Mineral Processing (No.BGRIMM-KJSKL-2021-13)High-end Foreign Expert Introduction Program (No.G2022105001L)State Key Laboratory of Comprehensive Utilization of LowGrade Refractory Gold Ores,Zijin Mining Group Co.,Ltd.
文摘In the past few decades,microbubble flotation has been widely studied in the separation and beneficiation of fine minerals.Compared with conventional flotation,microbubble flotation has obvious advantages,such as high grade and recovery and low consumption of flotation reagents.This work systematically reviews the latest advances and research progress in the flotation of fine mineral particles by microbubbles.In general,microbubbles have small bubble size,large specific surface area,high surface energy,and good selectivity and can also easily be attached to the surface of hydrophobic particles or large bubbles,greatly reducing the detaching probability of particles from bubbles.Microbubbles can be prepared by pressurized aeration and dissolved air,electrolysis,ultrasonic cavitation,photocatalysis,solvent exchange,temperature difference method(TDM),and Venturi tube and membrane method.Correspondingly,equipment for fine-particle flotation is categorized as microbubble release flotation machine,centrifugal flotation column,packed flotation column,and magnetic flotation machine.In practice,microbubble flotation has been widely studied in the beneficiation of ultrafine coals,metallic minerals,and nonmetallic minerals and exhibited superiority over conventional flotation machines.Mechanisms underpinning the promotion of fine-particle flotation by nanobubbles include the agglomeration of fine particles,high stability of nanobubbles in aqueous solutions,and enhancement of particle hydrophobicity and flotation dynamics.
文摘Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.
基金Supported by National Natural Science Foundation of China (Grant No.92148301)。
文摘The world is currently undergoing profound changes which have never happened within the past century.Global competition in the technology and industry fields is becoming increasingly fierce.The strategic competition of the major powers further focuses on the manufacturing industry.Developed countries such as the United States,Germany,and Japan have successively put forward strategic plans such as“re-industrialization”and“return of manufacturing industry”,aiming to seize the commanding heights of a new round of global high-end technology competition and expand international market share.Standing at the historic intersection of a new round of scientific and technological revolution and China's accelerated high-quality development,the“14th Five-Year Plan”clearly pointed out that intelligent manufacturing is the main development trend to promote China's manufacturing to the medium-high end of the global value chain.This reflects the importance of advanced manufacturing for national strategic layout.To better grasp the development direction of advanced manufacturing equipment,the development process and current application status of manufacturing equipment are summarized,and thereafter the characteristics of manufacturing equipment in different development stages of the manufacturing industry are analyzed.Finally,the development trend of advanced milling equipment is prospected.
基金supported by the National Key Research and Development Program of China(No.2021YFB2401200)。
文摘As the number of power terminals continues to increase and their usage becomes more widespread,the security of power systems is under great threat.In response to the lack of effective trust evaluation methods for terminals,we propose a trust evaluation model based on equipment portraits for power terminals.First,we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic.Second,we propose an exception evaluation method based on syntax and semantics.The key fields of each message are extracted,and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal.Thus,by combining the network flow order,syntax,and semantic analysis,an equipment portrait can be constructed to guarantee security of the power network terminals.We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time.Finally,the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate,as well as a higher real-time performance,which is more suitable for power terminals.
基金funded in part by the National Key R&D Program of China(Grant No.2022YFB3102901)the National Natural Science Foundation of China(Grant Nos.61976064,61871140,62272119,62072130)the Guangdong Province Key Research and Development Plan(Grant No.2019B010137004).
文摘To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.
基金supported by the Science and Technology Major Project 2020 of Liaoning Province,China(2020JH1/10100008)National Natural Science Foundation of China(61991404 and 61991400)111 Project 2.0(B08015)。
文摘Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.
基金supported by the National Natural Science Foundation of China[Grant No.61772160].
文摘Given the existing integrated scheduling algorithms,all processes are ordered and scheduled overall,and these algorithms ignore the influence of the vertical and horizontal characteristics of the product process tree on the product scheduling effect.This paper presents an integrated scheduling algorithm for the same equipment process sequencing based on the Root-Subtree horizontal and vertical pre-scheduling to solve the above problem.Firstly,the tree decomposition method is used to extract the root node to split the process tree into several Root-Subtrees,and the Root-Subtree priority is set from large to small through the optimal completion time of vertical and horizontal pre-scheduling.All Root-Subtree processes on the same equipment are sorted into the stack according to the equipment process pre-start time,and the stack-top processes are combined with the schedulable process set to schedule and dispatch the stack.The start processing time of each process is determined according to the dynamic start processing time strategy of the equipment process,to complete the fusion operation of the Root-Subtree processes under the constraints of the vertical process tree and the horizontal equipment.Then,the root node is retrieved to form a substantial scheduling scheme,which realizes scheduling optimization by mining the vertical and horizontal characteristics of the process tree.Verification by examples shows that,compared with the traditional integrated scheduling algorithms that sort the scheduling processes as an overall,the integrated scheduling algorithmin this paper is better.The proposed algorithmenhances the process scheduling compactness,reduces the length of the idle time of the processing equipment,and optimizes the production scheduling target,which is of universal significance to solve the integrated scheduling problem.
基金supported by the National Natural Science Foundation of China under Grants 62362040,61662033supported by the Science and Technology Project of the State Grid Jiangxi Electric Power Co.,Ltd.of China under Grant 521820210006.
文摘The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.
文摘The current space launch missions are intense, and the utilization of equipment is frequent, demanding increasingly higher responsiveness and capability in maintenance and support. The aerospace equipment maintenance and support chain relies on aerospace equipment maintenance and support facilities, deploying various maintenance and support resources rationally according to specific requirements and principles, ultimately forming a unidirectional functional chain or network from the supply side to the demand side. This system helps address the “bottleneck” issue in the generation of aerospace equipment support capability and significantly improves the level of aerospace equipment maintenance and support. The model construction is a prerequisite for analyzing the formation and operation mechanism of the chain, and identifying factors affecting the efficiency and effectiveness of maintenance and support. With consideration of the particularity of aerospace equipment maintenance and support, the paper extensively investigates the construction of the aerospace equipment maintenance and support chain model by drawing on research achievements in modern supply chain and logistics theories, as well as model construction methods. It develops a structural diagram-based chain model, with symbols as key elements, and establishes an evaluation indicator system, providing insights into understanding and grasping the composition of the aerospace equipment maintenance and support chain effectively. Furthermore, it offers a reference for solving other equipment support chains’ construction and optimization problems.
基金supported by the fund project:Research on Basic Capability ofMultimodal Cognitive Graph(Granted No.524608210192).
文摘Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect detection.At the same time,different types of intelligent sensors can mon-itor environmental information,such as temperature,humidity,dust,etc.Therefore,we apply the Internet of Things(IoT)technology to monitor the related environment and pervasive interconnections to diverse physical objects.However,the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems.Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph,which can be used to organize,share,and analyze equipment defect-related knowledge.At present,there are a lot of relevant data in the field of intelligent equipment defects.These equipment defect data often focus on a single aspect of the defect field.It is difficult to integrate the database with various types of equipment defect information.This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology.Based on ontology,this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities.This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field.The final experiment proves that this model is superior to other models in precision,recall,and F1 value.This research can break the barrier of multi-source heterogeneous knowledge,build an efficient storage engine for multimodal data,and empower the safety of Industrial applications,data,and platforms in multi-clouds for Internet of Things.