Fuzzy Dynamic Bayesian Network Research Articles

Dynamic evaluation of road and bridge engineering construction safety risk based on Fuzzy Dynamic Bayesian Network research

To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies.

A dynamic assessment method for risk evolution in chemical processes based on MFM-HAZOP-FDBN

At present, chemical accidents have occurred frequently with disastrous consequences. In order to effectively prevent chemical accidents and their secondary disasters, it is urgent to develop detailed risk assessment for chemical processes. However, traditional methods tend to ignore the dynamic correlation coupling of chemical processes and devices; this results in the assessment results having poor accuracy. Therefore, a dynamic assessment method for risk evolution in chemical processes based on MFM-HAZOP-FDBN is proposed in this study. First, multilevel flow model (MFM) is established according to the transfer paths of material flow and energy flow in a chemical process, and further combined with hazard and operability analysis (HAZOP) to form the functional HAZOP technology. Second, risk identification results from the MFM-HAZOP are applied as the input of a fuzzy dynamic Bayesian network (FDBN); the device states and process errors are used as dynamic and static nodes of FDBN to present the risk propagation and evolution mechanism in the chemical process, respectively. Finally, the dynamic risk evolutionary trend throughout the chemical process is assessed quantitatively by determining the occurrence likelihood and consequence severity based on the risk matrix. The BP America (Texas City) refinery explosion is taken as a case study. Results show that the proposed method can provide a detailed characterization and dynamic assessment of risk evolution in the chemical process and the assessment results are more reasonable and effective.

A dynamic system‐based model for analyzing human factors: Enhanced AcciMap with spherical fuzzy dynamic Bayesian network approach

AbstractIn today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision‐making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map‐based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real‐time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures.

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.

Fuzzy dynamic Bayesian network based on a discrete aggregation method for risk assessment of marine nuclear power platform hinge joints accidents

Hinge joints play a critical role in the positioning system of Marine Nuclear Power Platforms (MNPP) by facilitating the transmission of mooring forces and accommodating rotational motions of the hull. Failure of hinge joints during operation can result in severe consequences for both the environment and human safety. Therefore, this study aims to address the limitations associated with the dynamic characteristics and uncertain information pertaining to the risk systems of hinge joint structures in MNPP. To tackle the challenge of conflicting expert opinions in group decision-making, a novel method called the discrete aggregation method (DAM) is proposed. DAM utilizes fuzzy opinion aggregation, taking into account the degree of consensus among experts. This approach effectively overcomes the problem of conflicting opinions. Furthermore, building a hinge joint accidents model using Bow-tie (BT) for hazard identification, and then mapping the BT model to a dynamic Bayesian networks (DBN) model. Fuzzy set theory (FST) and DBN are employed to handle cognitive uncertainty caused by data limitations and incomplete knowledge. These techniques enable causal inference for time-varying dynamic risk systems. To validate the proposed method, a case study of the positioning system of the first MNPP in China is conducted. The dynamic fatigue failure probability of hinge joints is calculated using predictive technology based on Bayesian theory. Additionally, diagnostic analysis techniques are employed to accurately identify key events and their underlying causes. The analysis results provide valuable data support for on-site operations and management personnel, aiding in the development of effective risk mitigation measures.

Dynamic assessment of project portfolio risks from the life cycle perspective

Project portfolio risks (PPRs) are mostly considered in terms of interdependency between projects, ignoring the time dependency and causality between risks. This may lead to inappropriate risk assessments and reduced efficacy in risk treatments. This study aims to dynamically assess PPRs from the life cycle perspective to support managers in planning risk treatment actions more effectively. A fuzzy dynamic Bayesian network (F-DBN) is applicable in this scenario. First, PPRs are identified by considering project interdependency. Second, the causality and time dependency between the PPRs are modeled using an F-DBN. Then, the dynamic variation characteristics of the PPRs in the project portfolio (PP) life cycle are revealed. Finally, a numerical example is adopted to validate the applicability and effectiveness of the model. Based on the results, the key PPRs at different stages are identified, and the inherent characteristics with dynamic changes of those key risks are further revealed. The results of the analysis provide insights for PP managers to implement corresponding risk-reduction strategies.

Early warning method for overseas natural gas pipeline accidents based on FDOOBN under severe environmental conditions

The completion and operation of transnational long-distance oil and gas pipelines will not only alleviate shortages of oil and gas resources in China, but also lead to new development opportunities in the countries along their routes. However, the frequent occurrence of severe environmental disasters has led to several uncertainties regarding the long-term safe and stable operation of overseas pipelines, including pipeline fracture, fire, explosions, etc. In this paper, the fuzzy dynamic object-oriented Bayesian network (FDOOBN) theory was introduced to establish an early warning method for overseas natural gas pipeline accidents under harsh environmental conditions. Firstly, for the harsh environmental conditions (lightning, rain, and wind) at pipeline laying stations, accident scenarios under a single harsh environmental condition and the combination of multiple harsh environmental conditions were constructed. The object-oriented concept was adopted to modularize the station system and equipment, and the dynamic Bayesian network (DBN) model of each subsystem of the station was established. Then, the conditional probability parameters in the model were determined by the fuzzy mathematical method. The DBN model of each subsystem and the dynamic object-oriented Bayesian network (DOOBN) model of the entire station system based on the process flow of the station and the object-oriented concept were introduced in turn to establish the fuzzy dynamic Bayesian network (FDBN) model and the FDOOBN model respectively. The dynamic early warning system for station risk under harsh environmental conditions was finally realized. Finally, the prediction errors of environmental parameters, such as meteorological conditions, were introduced to modify the reliability of the model. The results show that compared with the traditional model, the error-corrected FDOOBN model not only has a better performance in simplifying the modelling process and fully integrating expert experience, but also has an increase in dynamic warning range, further improving the reliability of accident warnings.

A novel fuzzy dynamic Bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment

Risk assessment (RA) plays a vital role in safety engineering. The conventional RA approaches have limited capabilities in handling time dependence and data uncertainty. Although dynamic Bayesian network (DBN) is robust in inference under uncertainty due to its flexible structure and capability of modeling the interdependencies of variables, it still has some defects in quantifying the uncertainty (probability range) propagation over time, and dealing with inaccurate or insufficient data (data uncertainty). This study is aimed to propose a novel fuzzy dynamic Bayesian network (FDBN) methodology to improve the ability of dynamic risk assessment (DRA) methods to quantify and propagate uncertainty arise from inaccurate or insufficient data. The methodology incorporates the fuzzy set theory (FST) with DBN to conduct DRA under data uncertainty while quantifying the uncertainty propagation over time. The proposed methodology represents the causality of variables in the time dimension and adopts expert elicitation and FST to determine the probability of causality. Triangular fuzzy numbers are used throughout the entire dynamic modeling process of DBN to completely retain the uncertainty information. A comparison between the proposed novel FDBN and crisp value based DBN verifies the credibility, rationality and robustness of the proposed methodology. A multi-variable risk assessment of the cotton warehouse is presented here to illustrate the potential of the proposed methodology in dealing with dynamic risk with uncertainty.

A risk investment evaluation method based on dynamic bayesian network and fuzzy system

In order to enhance the risk investment evaluation algorithm precision of forestry rights mortgage of farmers, this paper provides a method of risk investment validating process of forestry rights mortgage of farmers based on dynamic Bayes network (DBN) and fuzzy system. For that have to be processed fuzzy data in time arrangement and evaluate the circumstance viably, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is assembled. Intuitionistic fuzzy thinking is implanted into DBN as a virtual node in this method. Also, another technique to change over the intuitionistic fuzzy thinking yield into likelihood that could contribution to DBN as proof is proposed. Firstly, it analyzes the risk investment of forestry rights mortgage of farmers, raises the risk evaluation system and adopts normalization and factor analysis methods to pre-process the model index; secondly, by aid of a four-layer DBN model, it puts forward the hierarchical DBN model of risk investment, having input layer, fuzzy layer, fuzzy inference layer and output layer, designs the composition and calculation mode of fuzzy function module and DBN module; Finally, it verifies the viability of the calculation through experimental examination.

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

Intuitionistic Fuzzy Dynamic Bayesian Network and its Application to Terminating Situation Assessment

The period of terminating situation in air combat is time-varying and full of information with uncertainty. In order to process the fuzzy information in time series and assess the situation effectively, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is built. In this model, intuitionistic fuzzy reasoning engine is embedded into dynamic Bayesian Network as a virtual node. Besides, a new method to convert the intuitionistic fuzzy reasoning output into probability which can be input into dynamic Bayesian Network as virtual evidence is proposed. On these bases, the detailed intuitionistic fuzzy reasoning algorithms are formulated and the dynamic Bayesian Network information transferring mode is analyzed. Finally, a case study for terminating situation assessment in air combat is given to prove the feasibility of the proposed model.

Network Information Security Situation Assessment Based on Bayesian Network

The situation of information security is difficult to be precise, autonomous and controllable. In this situation, the situation of the system is based on Fuzzy Dynamic Bayesian network. The model of situation awareness and situation estimation is constructed. The simulation results are compared with that of static Bayesian network model. The experimental results show that this method can better reflect the dynamic changes of network space operations.