Dynamic Bayesian Network Model Research Articles

Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach

Dynamic reliability assessment has offered a new paradigm for engineered systems to integrate multi-level observations to update the reliability measures of a specific running system. The existent work on dynamic reliability assessment can only leverage precise observations and cannot be straightforwardly implemented in scenarios where observations are imprecise. However, the observations are inevitably imprecise owing to the limited accuracy of inspection techniques and vague judgments of the system state. In engineering scenarios, multi-state systems (MSSs) with a hierarchical structure are commonly existent, and the imprecise observations can be collected across multiple physical levels of the system. In this article, the uncertainty associated with imprecise observations is characterized by the evidence theory, and it can be therefore regarded as an evidential form. A dynamic Bayesian network (DBN) model is utilized to evaluate the reliability of hierarchical MSSs with multi-level evidential observations. Subsequently, the evidence theory is implemented to quantify epistemic uncertainties associated with imprecise observations. These observations, sourced from multiple physical levels, are merged by the DBN model using the Dempster rule of combination (DRC) to update the reliability of a specific running system in a dynamic fashion. The feasibility and correctness of the proposed method have been demonstrated through a numerical case and a real engineering case of a kerosene filling control system (KFCS), and the result indicates that the proposed DBN-based algorithm methods have high applicability and generalizability.

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

Dynamic Bayesian network-based situational awareness and course of action decision-making support model

In this paper, we developed a dynamic Bayesian network (DBN) model to quantify uncertainties on battlefields. The model consists of the enemy's intention prediction model and the intelligence, surveillance, and reconnaissance (ISR) reliability model quantified by using the sensor detection probability. We calibrated and validated the DBN using a historical dataset based on actual provocations by comprehensively referencing open-source intelligence, including news articles from mass media and official announcements of the Ministry of National Defense. The calibrated model was then used to predict the enemy's intention in the near future, and the accuracy of the model was 84.6%. We suggested an appropriate course of action (COA) based on the enemy's intention to expedite decision-making. We further studied the interaction between the predicted enemy's intention and the selected COA. The main results of this paper are that various information regarding enemy actions, either equality or inequality, can be incorporated into the decision-making process.

A method for safety analysis of human-machine systems based on dynamic Bayesian simulation

Accidents in human-machine systems often lead to serious consequences, so safety analysis is very important for such systems. However, the existing approach to safety analysis of human-machine systems lacks clear delineation of the coupling relationships between human and machine, or provide quantitative analysis. To address these issues, this paper proposes a method for safety analysis of human-machine systems, utilizing dynamic Bayesian network (DBN) and dynamic fault tree (DFT). The core of this method is the identification of human-machine coupling relationships, proposing 10 types of logical relationships and presenting corresponding DFT logic. Then, a conversion method from DFT to DBN is designed to avoid combinatorial explosion in complex FTA calculations. Based on the DBN model, simulation is conducted using Gibbs sampling, which offers higher computational efficiency. Additionally, a method for importance analysis is devised to identify critical nodes that impact the system risk. At last, a case study of refueling mission at space launch site is given to illustrate how to apply the method. Through simulation analysis, the safety risks during the refueling mission are quantitatively assessed, while critical nodes are identified. The results indicate that the dynamic Bayesian simulation method is good in information utilization, dynamic representation, and time performance.

Study on reliability of control system in deepwater CBHP MPD system

Deepwater managed pressure drilling technology, with unique advantages in dealing with narrow density window, has been widely used in deepwater drilling operations. As the key equipment to realize managed pressure drilling, the control system in constant bottomhole pressure managed pressure drilling system plays an important role in drilling operations. In this study, a new reliability evaluation method based on fault tree analysis and dynamic Bayesian network model has been proposed to analyze the reliability of the control system. First, the structure of the control system in the deepwater managed pressure drilling system was analyzed to set up a fault tree model of the control system. Second, considering the influence of time on the reliability, a dynamic Bayesian network model of the control system was established. Third, the proposed model was partly verified using the verification method based on three axioms. Fourth, in consideration of the repair factor, the reliability and availability of the control system were calculated by using the exponential distribution model of failure time. Finally, the sensitivity of the control system was analyzed, and the mutual information, variance and entropy were calculated to find out weak links in key components of the control system.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation

The autonomy of the ship is bound to bring new risk challenges to its berthing/unberthing operations process. Potential damages resulting from these risks can range from minor hull deformations to substantial losses of assets and environmental harm. To address these risks in the process of berthing/unberthing operations of MASSs or mitigate them to an acceptable level, this paper aims to propose a risk analysis framework combining dynamic and static Bayesian network models, and which can consider the temporal correlation of the values of risk factors, to help ships avoid the occurrence of operational accidents. In this framework, a set of probabilistic model is included to calculate the real-time risk value of autonomous berthing operation, so as to analyze and solve the time-dependent problem of risk change with time during berthing/unberthing of autonomous ships. In addition, the developed model can determine the key influencing factors and possible key situations during the berthing/unberthing of autonomous ships, which can support the risk prevention and mitigation measures in the process of autonomous ship berthing/unberthing. Finally, the applicability of the proposed risk analysis framework is analyzed through a virtual simulation test case of autonomous berthing/unberthing.

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.

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

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.

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.

Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population.MethodsA cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models’ discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model’s effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software.ResultsThe area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%–80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count.ConclusionsA dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.

A dynamic Bayesian network model for resilience assessment in blockchain-based internet of medical things with time variation

Blockchain technology and the Internet of Medical Things (IoMT) have garnered increased attention recently due to their growing application in effectively managing data security, storage, and transmission concerns within healthcare organizations. However, integrating various advancements, such as coordination, adaptivity, and automated responses, within the framework of blockchain-based IoMT has amplified its susceptibility to a range of attacks and vulnerabilities. Assessing and enhancing the resilience of blockchain-based IoMT is of utmost importance, particularly in anticipation of potential disruptions, to ensure its continuous and sustainable functionality. The stochastic nature of risks adds complexity to evaluating the resilience of blockchain-based IoMT, given that resilience in this domain may fluctuate over time. This study employs a dynamic Bayesian network (DBN) method to address the evolving characteristics of pertinent variables, capturing their temporal dependencies and demonstrating how the resilience capabilities of blockchain-based IoMT may evolve across different time intervals. Additionally, an information theory approach is adopted to mitigate uncertainty regarding the resilience performance of blockchain-based IoMT and its crucial subcomponents. This research showcases the effectiveness and adaptability of the DBN methodology in healthcare systems, offering insights for shaping appropriate and essential strategies for decision-makers to establish a highly resilient framework for blockchain-based IoMT.

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships’ (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship’s configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems’ availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network

This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.

Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics

This study aims to analyze the risk of transporting hazardous chemicals on sea-crossing bridges using a dynamic Bayesian network (DBN) model that incorporates vehicle dynamics. Firstly, the cause-consequence relationship analysis is constructed using the bow-tie (BT) model, which is then translated into a Bayesian network (BN) by mapping algorithms. Based on the dynamic model, the occurrence probabilities of rollover and sideslip under different wind speeds are calculated as conditional probabilities. Secondly, a DBN model that satisfies the Markov assumption and time invariance is established to realize short-term risk prediction. Finally, the proposed model is applied to a sea-crossing bridge in Zhejiang, and other node parameters are obtained by combining the monitoring data of the vehicle-bridge transportation system (VBTS) monitoring platform and expert experience. The results indicate that vehicle failure has the highest impact on VBTS, and unsafe driver behavior and road alignment are the most vulnerable root causes, which should receive more attention. Additionally, wind sensitivity to VBTS is significant and cannot be ignored. The proposed method can effectively address the risks and challenges posed by hazardous chemical transportation on sea-crossing bridges and provides valuable insights with practical application to enhance transportation safety.

Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian

The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by the content validity ratio and content validity index; the T-S gate rule expressing causal uncertainty is constructed by using fuzzy theory and dependent uncertain ordered weighted averaging expert scoring, and finally, the pantograph T-S multistate fault tree is transformed into a DBN model characterizing the dynamic interaction and time dependence of the system. The dynamic evolution laws of reliability of a pantograph system in maintenance and maintenance-free states over time are inferred, compared and analyzed. The results show that the system availability of a pantograph system decreases continuously during 720 days of operation. The system availability without maintenance decreases to 0.881, and the system availability with maintenance is 0.952. The reliability of a pantograph system can be effectively ensured with maintenance during the operation period; the sensitivity analysis is performed by changing the failure rate of the equipment to 120% or 80%; the fall indicator, the electrical control box, and the elevating bow motor are the weak links in the system, and the impact of fault escalation on the reliability of a pantograph system is analyzed. It is then verified that the system reliability can be further improved by using a preventive maintenance strategy, and the steady-state reliability can be gradually reached, which is about 0.9968, providing a reference for the maintenance of a pantograph system.

A hybrid Dynamic Bayesian network method for failure prediction of a lock mechanism

This paper aims to construct a failure prediction method for a lock mechanism system to increase prediction accuracy. The two major failure modes in lock mechanisms are kinematic accuracy failure and clamping stagnation. One failure mode is affected by another failure mode as a result of multiple influencing factors that are dependent on one another. Besides, the characteristic values of failure modes are challenging to acquire in some particular situations, such as when sensors are not placed. To address these issues, this study aims to propose a hybrid Dynamic Gaussian Bayesian network (DGBN) model for the failure prediction of a lock mechanism, in which correlations between each influencing factor and correlations between each failure mode are taken into account simultaneously. The improved DGBN model is integrated with measurement data and system failure analysis. The presented model relies on the information about influence factors that are more easily measured in practice and can account for multiple hidden variables for which measurement data is missing. Furthermore, a failure prediction framework is developed based on the proposed model. Finally, the proposed prediction method is tested by the application of a lock mechanism. A comparison is made between the improved method and the data-driven method. The results show that the proposed method can predict failures relatively accurately, even when partial measurement data are missing. The prediction error narrowed from 10% to less than 4%.

A Novel Probabilistic Network Model for Estimating Cognitive-Gait Connection Using Multimodal Interface

Research in human gait analysis has captivated several computer vision researchers to solve human identification problems. The proposed work provides a novel approach for cognitive state estimation via multi-modal analysis. The advantage of multi-modal system is to provide adequate motion signatures with ensemble of multimodal gait data to ensure data reliability for classification. The relationship between human cognitive states and gait is predicted using both temporal and non-temporal probabilistic models. We estimate prior probability tables for non-temporal probabilistic model known as Simple Bayesian Model after analysing the data acquired from Inertial Measurement Unit (IMU), Electroencephalography (EEG) and multiple Kinect V2.0 sensors. A novel Dynamic Bayesian Network (DBN) is used as probabilistic temporal model for estimating most probable sequence of transitions among human cognitive states. We apply Gaussian Mixture Modelling with Expectation maximisation (GMM-EM) to tune transition and emission probabilities for maximizing the probability of the observed sequence. A promising estimation accuracy of 88.6% is obtained. Also, Principal Component Analysis (PCA) and k-nearest neighbour (kNN) algorithms are applied separately to calculate the input probabilities for DBN model. It is observed that both GMM-EM and k-NN based approaches outperform all the state-of-the-art techniques. Moreover, standard statistical tests are performed on acquired dataset to validate the experimental results.