Dynamic Bayesian Network Research Articles

Operational reliability analysis of remote operated vehicle based on dynamic Bayesian network synthesis method

In this study, we propose a reliability framework that combines the GO method with fault tree analysis and integrates human factor reliability analysis. Taking the application of remotely operated vehicle (ROV) in underwater oil and gas production as an example, a reliability model of ROV operation process is established. The human reliability in the ROV operation process is analyzed on the basis of Cognitive Reliability and Error Analysis Method (CREAM) extension approach. The influence of human factor reliability and equipment reliability on the system is compared and analyzed. The importance of each operation step is also compared and analyzed. A sensitivity analysis of the ROV during operation is performed. This study provides a theoretical basis for the improvement and maintenance of the operational reliability of ROV. The proposed reliability modeling approach solves the reliability analysis problem of large and complex operating systems.

Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

The welding of steel generates substantial welding residual stress (WRS), which exerts a significant impact on the fatigue life of steel bridges. In this study, a physical model for calculating the fatigue crack growth (FCG) life of welded specimens in the WRS field is established based on the weight function method. Experimental data validates the reliability and precision of the proposed physical model. The impact of WRS on the fatigue life of structural components is scrutinized and analyzed. On this basis, a digital twin (DT) framework driven by a physical-data model is proposed to consider the inherent parameter uncertainty in FCG behavior within the WRS domain. A dynamic Bayesian network (DBN) is used to characterize the evolution characteristics of fatigue crack states over time in the digital space. Particle filter algorithm is used as DBN inference method. The results show that the calculation of the physical model is in good agreement with the experimental values. Neglecting the influence of WRS distribution may lead to an overestimation of fatigue life in welded structures. The DT framework can update uncertain parameters online and realize the accurate prediction of the FCG life in the WRS field.

Learning dynamic embeddings for temporal attributed networks

Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.

System reliability calculation based on the run-time analysis of ladder program

Programmable logic controller (PLC) system, a typical member in the embedded family, is now widely applied in industry. For safety critical PLC systems, reliability is of top significance. However, due to subcomponents’ temporal correlations caused by the run-time execution of embedded ladder programs, the complexity of reliability analysis is greatly increased. In this paper, we propose a novel probabilistic model to analyze reliability of PLC systems, called run-time reliability model (RRM). RRM is automatically constructed based on the structure and run-time execution of the embedded ladder program. Moreover, it is also a dynamic bayesian network (DBN) capturing full dependencies in a PLC system. Then, according to execution semantics of RRM nodes, we present customized conditional probability distribution (CPD) tables to calculate final reliability of the system, with failure probability of every referenced component as refinement. The strength of this model is that not only does it explicitly specify the correlations between run-time execution of embedded software and system components, but also it serves as a computational mechanism for probabilistic inference. Besides, the proposed approach is superior to previous works in both accuracy and efficiency. Compared to monte carlo based simulation, the average error rate of reliability values inferred from RRM model is small.

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

With the popularization of the air conditioning, its reliability during operation has gradually become a focus of attention. However, due to the uncertainty in the reliability analysis process, the accuracy of the results will be affected. To overcome this challenge, a method for air conditioner reliability analysis combining Dynamic Bayesian Network (DBN) and Markov Model (MM) is proposed. Firstly, orthogonal defect classification (ODC) is used to statistic and analyze the defect data of the air conditioning system, and the network structure of the DBN is determined based on the results of the analysis. Then, the state transfer probability of each node is obtained by MM, and then the reliability, steady state availability, and maintainability of the air conditioning system are analyzed. Finally, the effectiveness of the method is verified by a case study of air conditioning failure data. The results show that the steady state availability of the air conditioning system in this case is 0.996.

Reliability assessment of dragline's subsystem using dynamic Bayesian network

Reliability assessment of dragline's subsystem using dynamic Bayesian network

Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation

Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation

A Systematic Monitoring Framework Utilizing Dynamic Bayesian Network in Plant-Wide Processes

A Systematic Monitoring Framework Utilizing Dynamic Bayesian Network in Plant-Wide Processes

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks

The text dissects the exploration of employing dynamic Bayesian networks (DBNs) for reliably assessing the risk posed to supply chain networks. A growing recognition of the complexity and intricacy of contemporary supply chain networks has sparked interest in novel methodologies such as DBNs that can model temporal and spatial dependencies effectively. DBNs emerge as a powerful analytical tool enabling probabilistic inference under prevailing uncertainties, capturing both the inherent and external risks. The paper demonstrates how these networks, bestowed with both temporal and causal mathematical frameworks, offer superior analytical traction over traditional methods in understanding the interconnected risk elements. We discuss their ability to appraise the ever-changing vulnerabilities in a supply chain setting, dynamically adapting to modifications brought by stakeholder actions or market changes. As supply chains burgeon in complexity, the role of DBNs in enabling a robust, comprehensive understanding of risk factors becomes increasingly paramount. Thus, the paper advocates for the DBNs' application as a transformative approach to risk assessment, providing significant foresight and adaptability in an unpredictable supply chain landscape.

Dynamic assessment of stakeholder conflict risk for R&D project portfolios

PurposeResearch and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.Design/methodology/approachThis study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.FindingsThe results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.Originality/valueThis study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

An integrated model to investigate optimized CO2 utilization and risk mitigation in offshore hydrocarbon reservoirs

This study addresses the dual challenge of balancing increasing energy demands with climate change mitigation through optimized carbon dioxide (CO2) utilization (CO2U) for enhanced oil recovery (EOR). This work presents a composite dynamic risk model for CO2-EOR operations, synergizing deterministic and data-driven methodologies for post-CO2 injection initiation. The primary focus is to gain a further understanding of the impact of CO2 injection on reservoir functionality, stability, and potential leaks in offshore deployment. The integrated novel model involves a spatial and temporal deterministic model with a data-driven strategy, incorporating an artificial neural network (ANN) fine-tuned via particle swarm optimization (PSO) for co-optimization of oil production and CO2 storage. Furthermore, an innovative early warning index system (EWIS) is proposed by integrating a dynamic Bayesian network (DBN) model for probabilistic loss evaluation and pressure maintenance impact assessment. The proposed DBN strategy generates dynamic risk profiles, facilitating a cost-efficient and computationally simpler risk monitoring system. The findings from the study show a recovery of 17.14 thousand barrels (Mbbls) from the total original oil-in-place (OOIP) through this CO2-EOR approach in its debut year, coupled with over 74% CO2 retention. The proposed model offers a robust tool for concurrent optimization of oil production and CO2 storage, whilst ensuring meticulous dynamic risk analysis throughout CO2U processes.

Fault Diagnosis of Airborne Electronic Equipment Based on Dynamic Bayesian Networks

With the rapid development of the aerospace industry, the structure of airborne electronic equipment has become more complex, which to some extent increases the difficulty of fault detection and maintenance of airborne electronic equipment. Traditional manual fault diagnosis methods can no longer fully meet the diagnostic needs of airborne electronic equipment. Therefore, this chapter uses dynamic Bayesian network to diagnose the faults of airborne electronic equipment. The basic idea of using a dynamic Bayesian network-based fault diagnosis method for airborne electronic devices is to mine data based on historical fault data of airborne electronic devices, and obtain fault symptoms and training data of airborne electronic devices. For non-essential fault symptoms, rough set theory was introduced to reduce their attributes and obtain the simplest attribute set, thereby simplifying the network model.

Risk evolution of crude oil pipeline under periodic maintenance based on dynamic bayesian network

Pipeline failure can result in a chain reaction of accidents, including oil and gas leaks. Traditional risk analysis methods fail to fully capture the degradation and imperfect maintenance processes of multi-state systems. The model utilizes a hybrid failure rate recursive rule to accurately depict the changes in system reliability under varying maintenance intervals. The robustness of the model was validated through a case study of crude oil pipelines and the probability of pipeline failure under different maintenance strategies was predicted. The results suggest that semi-annual maintenance can significantly reduce the probability of pipeline failure compared to annual maintenance. If the equipment has experienced multiple instances of imperfect maintenance, it will accumulate excessive fatigue damage, leading to a gradual increase in the growth rate of the hazard rate function. Therefore, after 36 months, the advantage of semi-annual incomplete maintenance gradually diminishes, indicating that there is an upper limit to the number of equipment maintenance actions. To maximize failure risk reduction and minimize losses, a combined maintenance strategy (semi-annual and annual maintenance) should be implemented. Finally, through sensitivity and diagnostic analysis, key factors leading to pipeline failure were identified and appropriate measures to prevent pipeline failure were proposed.

Dynamic risk assessment method for urban hydrogen refueling stations: A novel dynamic Bayesian network incorporating multiple equipment states and accident cascade effects

Hydrogen refueling stations are increasingly being built in densely populated urban areas and operating under high temperature and pressure conditions. Therefore, the safety of hydrogen refueling stations has received great attention. In present work, a new dynamic quantitative risk assessment method is established for hydrogen leakage at refueling stations based on Bayesian networks (BN). Firstly, risk factors are identified using a fault tree, considering multiple equipment operating states. A Dynamic Bayesian Network (DBN) is established, and event tree analysis is combined to determine accident consequence probabilities. Furthermore, a Bayesian Domino Model is constructed to assess the impact of accident consequence propagation on the probability and risk of hydrogen leakage accidents. Through a typical case study of an off-site hydrogen refueling station, the practicality of the proposed method is validated, the risk factors and safety barriers, the dynamic evolution of hydrogen leakage probabilities within a three-year period are obtained. The probability of hydrogen leakage in hydrogen station is 2.69 × 10−2 in the first month and 2.45 × 10−2 in the 36th month. Simultaneously, the maximum personal risk value for the first month is 5.07 × 10−4. When the Domino effect is considered, the maximum accident probability is 1.96 × 10−3 is obtained in the first month, and the equipment unit with the greatest impact of the accident is A2. Measures were also proposed to reduce hydrogen leakage risks during the operation of off-site hydrogen refueling stations, with an emphasis on prioritizing safety measures for hydrogen storage bundles and risk mitigation strategies.

Flammable gas leakage risk assessment for methanol to hydrogen refueling stations and liquid hydrogen refueling stations

Due to its excellent combustion properties and environmental performance, the development of hydrogen energy has become an international trend in recent years. However, hydrogen refueling stations, which are the infrastructure for hydrogen energy development, contain flammable gases such as hydrogen and methanol, which are highly susceptible to explosion and may threaten people's health and property safety. In this paper, a dynamic risk assessment method based on Dynamic Bayesian Network (DBN) is established for combustible gas leakage accidents in hydrogen refueling stations, and the methanol to hydrogen refueling stations and liquid hydrogen refueling stations are taken as research objects to quantitatively analyze their leakage risks and accident consequences. It is found that the leakage accident probability and consequences of methanol to hydrogen refueling stations show periodicity with a period of three months, while there is no periodicity for liquid hydrogen refueling stations. The probabilities of leakage accident and serious consequences in methanol to hydrogen refueling stations show a sudden increase within a cycle, on the other hand, those in liquid hydrogen refueling stations tend to increase linearly over three years with time. In addition, based on the diagnostic reasoning function of DBN, this paper identifies the two most critical hazard sources for flammable gas leakage accident in hydrogen refueling stations: human factors and corrosion factors, and provides guidance for relevant enterprises to reduce the risk.

A Three-Stage Dynamic Risk Model for Metro Shield Tunnel Construction

The complex construction process of the metro shield method often leads to safety accidents. The various construction stages of shield tunnel construction comprise different construction activities and are accompanied by different safety risk factors. However, traditional risk assessment often evaluates the risk factors as a whole before shield tunnel construction and does not evaluate the risk factors dynamically by construction stages and by construction activities. To fill this gap, this paper aims to construct a dynamic Bayesian-based safety risk assessment model for shield tunnel construction from the perspective of changing construction stages and activities. First, safety risk factors were identified using the work breakdown structure-risk breakdown structure (WBS-RBS) method. Then, a three-stage dynamic assessment model of safety risks was constructed to depict the shield launch, shield tunnel, and shield reach. The dynamic Bayesian network (DBN) was improved to address the model with triangular fuzzy numbers and the leaky noisy-or-gate extension model. Finally, a case study was conducted. The model proposed in this paper is able to reveal the dynamic evolution of safety risks triggered by different construction activities. It offers a new simulated model for the prevention of safety accidents in the construction of metro shields.

Extracting Bayesian networks from multiple copies of a quantum system

Despite their theoretical importance, dynamic Bayesian networks associated with quantum processes are currently not accessible experimentally. We here describe a general scheme to determine the multi-time path probability of a Bayesian network based on local measurements on independent copies of a composite quantum system combined with postselection. We further show that this protocol corresponds to a nonprojective measurement. It thus allows the investigation of the multi-time properties of a given local observable while fully preserving all its quantum features.

A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems

Abstract Unmanned engineering systems that execute various operations are becoming increasingly complex relying on a large number of components and their interactions. The reliability, maintainability, and performance optimization of these systems are critical due to their intricate nature and inaccessibility during operations. This paper introduces a new reliability-based optimization framework for planning operational profiles for unmanned systems. The proposed method employs deep learning techniques for subsystem health monitoring, dynamic Bayesian networks for system reliability analysis, and multi-objective optimization schemes for optimizing system performance. The proposed framework systematically integrates these schemes to enable their application to a wide range of tasks, including offline reliability-based optimization of system operational profiles. This framework is the first in the literature that incorporates health monitoring of multi-component systems with causal relationships. Using this hybrid scheme on unmanned systems can improve their reliability, extend their lifespan, and enable them to execute more challenging missions. The proposed framework is implemented and executed using a simulation model for the engine cooling and control system of an unmanned surface vessel.

Thriving arid oasis urban agglomerations: Optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.