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Combined Fault Tree Analysis and Bayesian Network for Reliability Assessment of Marine Internal Combustion Engine 认领 引用 被引量：1
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作者 Ivana Jovanović Çağlar Karatuğ +1 位作者 Maja Perčić Nikola Vladimir 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期239-258,共20页
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ... This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels. 展开更多
关键词 Fault tree analysis Bayesian network Reliability Redundancy Internal combustion engine
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Study of meteorological‐hydrological drought propagation under reservoir regulation using a Copula‐Bayesian network in the Hanjiang River Basin 认领 引用
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作者 Yanping Qu Cheng Li +3 位作者 Yachao Zhang Siyu Zhao Tianliang Jiang Qinghua Ye 《River》 EI CSCD 2026年第1期33-54,共22页
Reservoir operations play a pivotal role in modifying drought propagation processes,particularly by influencing the transition from meteorological to hydrological drought.This study investigates the drought propagatio... Reservoir operations play a pivotal role in modifying drought propagation processes,particularly by influencing the transition from meteorological to hydrological drought.This study investigates the drought propagation characteristics in the middle reaches of the Hanjiang River Basin,China,under both natural and observed(reservoir‐influenced)conditions.The Standardized Precipitation Evapotranspiration Index and Standardized Streamflow Index were utilized to characterize meteorological and hydrological drought,respectively.The Soil and Water Assessment Tool was employed to reconstruct natural streamflow,providing a baseline for comparison.A nonlinear copula function was applied to model the dependence between meteorological and hydrological drought characteristics,and a Copula‐Bayesian network was developed to quantify propagation probabilities.Under the regulation of the Danjiangkou Reservoir,drought propagation characteristics for 1–12‐month timescales have shifted markedly:the average propagation time downstream was prolonged from 0.25–0.70 months to 0.94–2.36 months,while the propagation rate declined from 0.83–0.89 to 0.48–0.65,and the sensitivity decreased from 0.83–0.96 to 0.68–0.79.In the natural scenario,the optimal propagation model was based on the Gumbel copula,whereas the observed scenario was best fitted by the Frank copula.The likelihood of hydrological drought increased with the intensity and duration of meteorological drought.However,compared to natural conditions,reservoir regulation significantly delayed the onset and reduced the probability of hydrological drought occurrence.These findings elucidate the nonlinear dynamics of drought propagation and underscore the regulating effect of large‐scale reservoirs on downstream hydrological responses. 展开更多
关键词 Copula‐Bayesian network Danjiangkou Reservoir drought propagation hydrological drought meteorological drought
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Performance improvement method of new R&D institutions considering Bayesian network 认领 引用
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作者 ZHU Jianjun JIANG Lin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期257-271,共15页
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys... A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method. 展开更多
关键词 new research and development(R&D)institution performance improvement evolutionary game Bayesian network conditional probability
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Defect Identification Method of Power Grid Secondary Equipment Based on Coordination of Knowledge Graph and Bayesian Network Fusion 认领 引用
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作者 Jun Xiong Peng Yang +1 位作者 Bohan Chen Zeming Chen 《Energy Engineering》 EI 2026年第1期296-313,共18页
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo... The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency. 展开更多
关键词 Knowledge graph Bayesian network secondary equipment defect identification
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Sea ice collision risk assessment based on Bayesian network modeling 认领 引用
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作者 LI Xianling WANG Zixin +3 位作者 ZHANG Haibin HE Jinhui WANG Yanlin HUANG Xiaoming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期579-593,共15页
To address the problem of sea ice collisions threatening offshore drilling operations in polar regions,this paper proposes a Bayesian network-based collision risk assessment model for drillships.The model integrates l... To address the problem of sea ice collisions threatening offshore drilling operations in polar regions,this paper proposes a Bayesian network-based collision risk assessment model for drillships.The model integrates large ice floe/iceberg conditions,natural environmental factors,and geometric factors derived from the ship’s shape,size,distance,and azimuth.Using iceberg routes,scenario simulations are conducted to evaluate collision probabilities and provide time-dependent risk values.Results demonstrate that the method yields reasonable and consistent assessments of drillship-ice interactions.The proposed method enables automatic collision risk assessment and can be applied to unattended management systems to enhance the safety of polar drilling operations. 展开更多
关键词 Bayesian network risk assessment drillship ice floe/iceberg collision
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Hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks in the noisy intermediate-scale quantum era 认领 引用
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作者 Hao Shi Chenghao Han +1 位作者 Peng Wang Ming Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第12期61-74,共14页
Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources... Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era. 展开更多
关键词 quantum Bayesian networks multi-agent decision-making hybrid quantum–classical algorithms hierarchical Bayesian networks
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Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning 认领 引用
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作者 Yuan-Chien Lin Yu-Ting Lin +1 位作者 Cai-Rou Chen Chun-Yeh Lai 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第6期54-70,共17页
Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air qual... Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO2,and NOx;and O3,PM10,and PM2.5,respectively. 展开更多
关键词 Air quality Rainfall pattern Traffic emissions Generalized additive model Bayesian networks LSTM model
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Dynamic Reliability Assessment Approach for Deepwater Subsea Wellhead Systems via Hybrid Bayesian Networks 认领 引用
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作者 LI Jia-yi CHANG Yuan-jiang +2 位作者 LIU Xiu-quan XU Liang-bin CHEN Guo-ming 《China Ocean Engineering》 SCIE EI CSCD 2025年第1期100-110,共11页
The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic ... The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life. 展开更多
关键词 deepwater subsea wellhead system reliability accumulation fatigue damage hybrid Bayesian network
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Comprehensive review of Bayesian network applications in gastrointestinal cancers 认领 引用
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作者 Min-Na Zhang Meng-Ju Xue +4 位作者 Bao-Zhen Zhou Jing Xu Hong-Kai Sun Ji-Han Wang Yang-Yang Wang 《World Journal of Clinical Oncology》 2025年第6期45-63,共19页
Gastrointestinal cancers,including esophageal,gastric,colorectal,liver,gallbladder,cholangiocarcinoma,and pancreatic cancers,pose a significant global health challenge due to their high mortality rates and poor progno... Gastrointestinal cancers,including esophageal,gastric,colorectal,liver,gallbladder,cholangiocarcinoma,and pancreatic cancers,pose a significant global health challenge due to their high mortality rates and poor prognosis,particularly when diagnosed at advanced stages.These malignancies,characterized by diverse clinical presentations and etiologies,require innovative approaches for improved management.Bayesian networks(BN)have emerged as a powerful tool in this field,offering the ability to manage uncertainty,integrate heterogeneous data sources,and support clinical decision-making.This review explores the application of BN in addressing critical challenges in gastrointestinal cancers,including the identification of risk factors,early detection,treatment optimization,and prognosis prediction.By integrating genetic predispositions,lifestyle factors,and clinical data,BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies.Despite their promise,the widespread adoption of BN is hindered by challenges such as data quality limitations,computational complexities,and the need for greater clinical acceptance.The review concludes with future research directions,emphasizing the development of advanced BN algorithms,the integration of multi-omics data,and strategies to ensure clinical applicability,aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers. 展开更多
关键词 Gastrointestinal cancers Bayesian networks Heterogeneous data integration Early detection Risk prediction Prognosis Personalized medicine
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A Knowledge Push Method of Complex Product Assembly Process Design Based on Distillation Model-Based Dynamically Enhanced Graph and Bayesian Network 认领 引用 被引量：1
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作者 Fengque Pei Yaojie Lin +2 位作者 Jianhua Liu Cunbo Zhuang Sikuan Zhai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第6期117-134,共18页
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a... Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design. 展开更多
关键词 Complex product assembly process Large language model Dynamic incremental construction of knowledge graph Bayesian network Knowledge push
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Integrated fuzzy fault tree-Bayesian network modeling for rollover risk assessment of LNG road tankers 认领 引用
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作者 Liu Yang Ying Zhang +3 位作者 Qike He Zhiyong Lv Dongyang Qiu Sining Chen 《Emergency Management Science and Technology》 2025年第1期77-87,共11页
Liquefied natural gas(LNG)road tanker rollover accidents,though infrequent,often lead to catastrophic consequences.Quantitative risk assessment is significantly challenged by the scarcity of probabilistic data specifi... Liquefied natural gas(LNG)road tanker rollover accidents,though infrequent,often lead to catastrophic consequences.Quantitative risk assessment is significantly challenged by the scarcity of probabilistic data specific to these complex,low-frequency events.To address this data limitation and enhance assessment accuracy,this study develops an integrated fuzzy fault tree-Bayesian network(FFT-BN)methodology.Fuzzy set theory is applied,leveraging multi-source general traffic accident statistics and expert judgment,to quantify the occurrence probabilities of basic causal factors under uncertainty.A Bayesian network is then constructed from the fault tree structure to enable comprehensive probabilistic inference.Critical risk factors were rigorously identified using multiple importance measures(ROV,BIM,RRW).The analysis consistently pinpointed poor road alignment and the absence of critical traffic facilities as the two paramount contributors.Crucially,vehicle speed management emerged as the central mitigation mechanism linking these factors;controlling speed effectively counters the destabilizing effects of poor alignment and compensates for the lack of timely hazard perception.The results demonstrate that implementing targeted speed control measures on identified high-risk road sections is essential for reducing the probability of LNG tanker rollovers. 展开更多
关键词 risk assessment Bayesian network rollover risk assessment expert judgmen LNG road tanker probabilistic data set theory fuzzy fault tree
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Risk assessment of syngas pipeline leakage based on a Dynamic Bayesian Network 认领 引用
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作者 Yuqi Liu Min Hua Xuhai Pan 《Emergency Management Science and Technology》 2025年第1期146-155,共10页
furnace,is vulnerable to corrosion and erosion from the transported medium,and is prone to leakage due to long-term high-pressure operation.Moreover,due to its compositional characteristics,syngas pose flammability,ex... furnace,is vulnerable to corrosion and erosion from the transported medium,and is prone to leakage due to long-term high-pressure operation.Moreover,due to its compositional characteristics,syngas pose flammability,explosive,and toxicity risks,which can potentially lead to severe accidents if a leak occurs.Therefore,it is essential to conduct a risk assessment of syngas pipeline leakage.This study proposes a risk assessment approach for syngas pipeline leakage in coal gasification using Dynamic Bayesian Network(DBN).First,the risk identification model is built using Bow-Tie(BT)analysis and then mapped into DBN using a mapping algorithm.Expert evaluation,improved similarity aggregation methods,and fuzzy set theory are employed to quantify prior probabilities.To address the uncertainty of the DBN model,a Leak Noisy-or gate model is introduced.Time series are added to predict the dynamic probabilities.Nine key hazard events,six highly sensitive factors,and the maximum causal chain are identified,and predict the dynamic probability of syngas pipeline leakage and potential consequences.This study provides a theoretical basis for routine maintenance and risk assessment of syngas pipelines. 展开更多
关键词 risk assessment dynamic bayesian network fuzzy set theory dynamic bayesian netwo leakage bow tie analysis coal gasification syngas pipeline
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Optimization of High-Speed Railway Bridge Disaster Warning Systems Using Fuzzy Bayesian Networks and Embedded Runge-Kutta Pairs for Real-Time Risk Assessment 认领 引用
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作者 Yufei Zhang Hongyu Liang +5 位作者 Yuebing Liu Yuxin Fan Qiuyan Chen Wenjie Fu Genxi Wang Yucheng Shen 《Journal of World Architecture》 2025年第6期63-72,共10页
High-speed railway(HSR)bridges face multi-hazard risks from wind,earthquakes,and fires,necessitating optimized warning systems for safety and efficiency.This study proposes a framework integrating fuzzy Bayesian netwo... High-speed railway(HSR)bridges face multi-hazard risks from wind,earthquakes,and fires,necessitating optimized warning systems for safety and efficiency.This study proposes a framework integrating fuzzy Bayesian networks(FBNs)for probabilistic risk modeling with embedded Runge-Kutta pairs for dynamic simulations,enabling real-time assessments.FBNs handle uncertainties across 22 indicators from standards like GB 50352-2019,categorizing capabilities in prevention,extinguishing,evacuation,rescue,and management.Runge-Kutta(orders 6/5)solves ODEs for transient responses,approximating finite element outputs in surrogates like LSTM-RNNs.Alarm optimization uses objective functions balancing busyness and alarm frequency,tested on Chinese HSR lines.Results show wind alarm reductions up to 47.6%with minimal downtime increases,fire risks graded“good”(54.3%)with management as key improvement,and collision missed alarms<6%at<1ms.The system reduces missed warnings by 6%and delivers alerts in milliseconds,advancing HSR resilience and minimizing disruptions. 展开更多
关键词 Disaster warning Fuzzy Bayesian network High-speed railway Runge-Kutta Risk assessment
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Reliability analysis for wireless communication networks via dynamic Bayesian network 认领 引用 被引量：1
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作者 YANG Shunqi ZENG Ying +2 位作者 LI Xiang LI Yanfeng HUANG Hongzhong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第5期1368-1374,共7页
The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works ... The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network. 展开更多
关键词 dynamic Bayesian network(DBN) wireless commu-nication network continuous time Bayesian network(CTBN) network reliability
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Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks 认领 引用 被引量：19
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作者 Haoyu Mao Nuwen Xu +4 位作者 Xiang Li Biao Li Peiwei Xiao Yonghong Li Peng Li 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第10期2521-2538,共18页
One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the ev... One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the evolutionary mechanism of microfractures within the surrounding rock mass during rockburst development and develop a rockburst warning model.The study area was chosen through the combination of field studies with an analysis of the spatial and temporal distribution of microseismic(MS)events.The moment tensor inversion method was adopted to study rockburst mechanism,and a dynamic Bayesian network(DBN)was applied to investigating the sensitivity of MS source parameters for rockburst warnings.A MS multivariable rockburst warning model was proposed and validated using two case studies.The results indicate that fractures in the surrounding rock mass during the development of strain-structure rockbursts initially show shear failure and are then followed by tensile failure.The effectiveness of the DBN-based rockburst warning model was demonstrated using self-validation and K-fold cross-validation.Moment magnitude and source radius are the most sensitive factors based on an investigation of the influence on the parent and child nodes in the model,which can serve as important standards for rockburst warnings.The proposed rockburst warning model was found to be effective when applied to two actual projects. 展开更多
关键词 Microseismic monitoring Moment tensor Dynamic Bayesian network(DBN) Rockburst warning Shuangjiangkou hydropower station
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A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization 认领 引用 被引量：21
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作者 JI Jun-Zhong ZHANG Hong-Xun +1 位作者 HU Ren-Bing LIU Chun-Nian 《自动化学报》 EI CAS 北大核心 2009年第3期281-288,共8页
To solve the drawbacks of the ant colony optimization for learning Bayesian networks(ACO-B),this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization(I-ACO-B).Fir... To solve the drawbacks of the ant colony optimization for learning Bayesian networks(ACO-B),this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization(I-ACO-B).First,the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions,so that many unnecessary searches of ants can be avoided.And then,by combining the global score increase of a solution and local mutual information between nodes,a new heuristic function with better heuristic ability is given to induct the process of stochastic searches.The experimental results on the benchmark data sets show that the new algorithm is effective and efficient in large scale databases,and greatly enhances convergence speed compared to the original algorithm. 展开更多
关键词 Uncertainty modeling Bayesian network structure learning ant colony optimization(ACO) conditional independencetest
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Evaluation and intelligent deployment of coal and coalbed methane coupling coordinated exploitation based on Bayesian network and cuckoo search 认领 引用 被引量：8
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作者 Quanle Zou Zihan Chen +6 位作者 Zhiheng Cheng Yunpei Liang Wenjie Xu Peiran Wen Bichuan Zhang Han Liu Fanjie Kong 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2022年第6期1315-1328,共14页
Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination betwe... Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future. 展开更多
关键词 Coal and coalbed methane Coupling coordinated exploitation Bayesian network Cuckoo search Intelligent optimization
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Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults 认领 引用 被引量：5
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作者 Fangjun Zuo Meiwei Jia +2 位作者 Guang Wen Huijie Zhang Pingping Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第11期993-1012,共20页
In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditiona... In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools. 展开更多
关键词 Bayesian network(BN) dynamics fuzzy multi-state
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Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network 认领 引用 被引量：10
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作者 Yun-Tao Li Xiao-Ning He Jian Shuai 《Petroleum Science》 SCIE CAS CSCD 2022年第3期1250-1261,共12页
Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time.Identifying the causes of external corrosion and taking specific maintenance measures is... Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time.Identifying the causes of external corrosion and taking specific maintenance measures is essential.In this work,a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network.A fault tree model is first employed to identify the causes of external corrosion.The Bayesian network for risk analysis is determined accordingly.The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk.The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model.Because of the limitations of historical data,some of the parameters in the Bayesian network are obtained from a probabilistic estimation model,which combines expert experience and fuzzy set theory.Finally,a case study is carried out to verify the feasibility of the maintenance decision model.This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion. 展开更多
关键词 Natural gas pipelines External corrosion Risk analysis Maintenance decision making Bayesian network
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Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach 认领 引用 被引量：15
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作者 Zhengdao Zhang Jinlin Zhu Feng Pan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第3期500-511,共12页
For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-d... For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements. 展开更多
关键词 fault detection and diagnosis Bayesian network Gaussian mixture model data incomplete non-imputation.
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