Dynamic Bayesian Network Model Research Articles

Dynamic risk assessment of plugging and abandonment operation process of offshore wells based on Dynamic Bayesian Network

The plugging and abandonment (P&A) operation is required when an offshore well is abandoned. This study proposes an analysis method based on dynamic Bayesian network (DBN) to assess the dynamic risk of riserless well intervention systems performing the P&A operation. First, each phase of P&A operation is depicted by a GO model. The fault tree (FT) model of each mission execution system is constructed to analyze the equipment factors leading to the failure of the mission. The mirror DBN model is established from the GO and FT models according to the mapping algorithm and the parameters of the DBN model are obtained by mapping according to the GO and FT model parameters. A complete risk assessment model is established. Next, the standardized plant analysis risk-human reliability analysis method is used to quantify the probabilities of human factors in the DBN model. Finally, the dynamic risk of the operation process is assessed. The developed DBN is verified using a method based on three axioms. Results show that the reservoir abandonment phase has the greatest effect on P&A operation process risk. Sensitivity analysis is performed to identify critical equipment of the system. The proposed method can be a useful tool to assess the dynamic risk of P&A operation process.

Learning the structure of the mTOR protein signaling pathway from protein phosphorylation data

Statistical learning of the structures of cellular networks, such as protein signaling pathways, is a topical research field in computational systems biology. To get the most information out of experimental data, it is often required to develop a tailored statistical approach rather than applying one of the off-the-shelf network reconstruction methods. The focus of this paper is on learning the structure of the mTOR protein signaling pathway from immunoblotting protein phosphorylation data. Under two experimental conditions eleven phosphorylation sites of eight key proteins of the mTOR pathway were measured at ten non-equidistant time points. For the statistical analysis we propose a new advanced hierarchically coupled non-homogeneous dynamic Bayesian network (NH-DBN) model, and we consider various data imputation methods for dealing with non-equidistant temporal observations. Because of the absence of a true gold standard network, we propose to use predictive probabilities in combination with a leave-one-out cross validation strategy to objectively cross-compare the accuracies of different NH-DBN models and data imputation methods. Finally, we employ the best combination of model and data imputation method for predicting the structure of the mTOR protein signaling pathway.

Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines

Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability.

Selection of Areas for Effective GNSS Spoofing Attacks to a Vehicle-mounted MSF System based on Scenario Classification Models

The inherent vulnerability of the Global Navigation Satellite System (GNSS) leads to the ease of implementation of spoofing attacks. The latest GNSS spoofing attack schemes still suffer from low success rate, long attack time, and poor concealment. To improve the success rate, an efficient GNSS spoofing attack method for a vehicle-mounted Multi Sensors Fusion (MSF) system is proposed based on the scenario classification models with a spatial database. Firstly, the influence of the two typical urban scenarios, which are 1) the road with buildings on both sides and 2) tunnels, on the GNSS spoofing attack is analyzed. Then a dynamic Bayesian network model considering the sky visibility generated with the 3D building models and tunnel models inside the spatial database is established to quantify the difficulty of the attack. Furthermore, the scenarios of the victim can be classified into high-risk and low-risk scenarios. When the vehicle is just out of the tunnel or in open scenarios, attackers can select these high-risk scenarios and implement aggressive spoofing attacks. Then the efficiency of the GNSS spoofing attack can be significantly improved. Finally, the proposed attack scheme is demonstrated by actual world data with simulated spoofing attacks in urban areas.

Dynamic risk assessment of deepwater drilling using data-based and probabilistic approach

Risks associated with deepwater drilling are dynamic because of various accident-causing factors such as equipment failures, abnormal processes, and operator errors. All these factors should be considered during the implementation of dynamic and quantitative risk assessment (DQRA) for drilling operations. Dynamic Bayesian network (DBN) can graphically present the cause-effect relationships among different types of risk influencing factors (RIFs). Hence, this study developed a four-step DBN model for the DQRA of deepwater drilling. Firstly, multi-type contributing RIFs were identified according to the process flow. Subsequently, a network structure was developed to present the potential accident scenarios and capture the interdependencies among the RIFs. Thereafter, the probabilities of equipment failures, abnormal processes, and operator errors were determined using the probabilistic, data-based, and Standardized Plant Analysis Risk—Human Reliability Analysis (SPAR-H) approach, respectively. Finally, DBN inference was performed to evaluate the probabilistic risk of drilling operations. The model was applied to a case study of DQRA for managed pressure drilling (MPD), where the calculated initial blowout probability was 9.30 × 10−5, whereafter it was updated dynamically. This case study demonstrates the practicability of the proposed approach. This study contributes to a systematic investigation of the role of multisource data in DQRA using a full DBN approach. The assessment results can provide early warnings for practitioners to implement risk elimination or mitigation measures in real time.

Risk Response Strategies Selection over the Life Cycle of Project Portfolio

The successful implementation of project portfolios (PP) calls for effective risk management, in which selecting optimal risk response strategies help to reduce the impact of risk. Project portfolio risks (PPRs) exhibit causality and time dependency over the life cycle, which result in cumulative effects over time. By accounting for these risk correlations, risk response could be more effective in reducing expected losses than risk independence assumption. To support effective and sustainable risk management, this study proposes a novel risk response method that integrates the dynamic Bayesian network (DBN) model and reward–risk optimization model to select risk response strategies for different stages of the PP life cycle. The proposed method supports a more comprehensive analysis of risk contagion paths by opening the black box of the risk propagation paths during the PP life cycle. In this method, the PPRs, as the DBN nodes, are first identified, considering the project’s interdependency. Second, DBN analysis is used to assess PPRs by visually modeling the causality and life cycle correlation among risks. Then, the reward–risk optimization model is built to determine risk response strategies for each stage of the life cycle under the constraints. Finally, the proposed method selects risk response strategies for different stages of the PP life cycle. The findings reveal that the risk response effects are maximized if the risks are responded to in earlier stages. Moreover, the findings contribute to helping managers choose the optimal risk response strategies consistent with the risk response budget. As the effect of the strategy depends on the actual situation of the PP, the factors affecting the response effect of the strategies are recommended for further study.

Dynamic Failure Risk Assessment of Wastewater Treatment and Reclamation Plant: An Industrial Case Study

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality.

Framework for Process Analysis of Maritime Accidents Caused by the Unsafe Acts of Seafarers: A Case Study of Ship Collision

Accurately describing and evaluating the effects of unsafe acts on maritime accidents is critical to establishing practical accident prevention and control options. This paper proposes a framework for the probabilistic analysis of maritime accidents caused by seafarers’ unsafe acts by incorporating a navigation simulation and dynamic Bayesian network (DBN) modeling. First, the unsafe acts of seafarers are identified according to an in-depth analysis of global maritime investigation reports. Then, a navigation simulation experiment is designed to collect the ship-handling data of seafarers during hazardous accident scenarios. Consequently, a dynamic probabilistic model is proposed using a DBN to describe the phases of maritime accidents based on the navigation simulation experiment data. Furthermore, an evolution analysis of maritime accidents is conducted to explore the causal chain of such accidents through sensitivity analysis. The typical navigational accident-collision is chosen as the case to interpret the proposed framework, considering the formation process of ship collision risks, from the occurrence of ship collision risk (phase 1) to the close-quarters situation (phase 2) and to immediate danger (phase 3). This framework is applied to explore the causal chain of collision accidents caused by the unsafe acts of seafarers.

Evaluating Gene Regulatory Network Activity From Dynamic Expression Data by Regularized Constraint Programming.

By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g., transcription factor proteins and miRNAs, and target genes. Network activity is defined as the degree of consistency between a regulatory network architecture and a specific cellular context of gene expression and can also be measured as a score of statistical significance. The gene network activities are closely related to the dynamics of cell states. To evaluate the activity of regulatory events in the form of network, we propose a network activity evaluation (NAE) framework by measuring the consistency between network architecture and gene expression data across specific states based on mathematical programming. NAE firstly employs the dynamic Bayesian network model to formulate the network structure with time series profiling data. For the constraints of prior knowledge about gene regulatory network, NAE introduces an interpretable general loss function with regularization penalties to calculate the degree of consistency between gene network and gene expression data. Moreover, we design a fast and convergent alternating direction method of multipliers algorithm to optimize the regularized constraint programming. The efficiency and advantage of the NAE framework is deduced through numerous experiments and comparison studies. It reflects the possibility and potential of the match between network and data, thereby helping to reveal the network activity and to explain the dynamic responds underlying the network structure caused by changes in molecular environment of living cells. The code of NAE is freely available for academic use (https://github.com/zpliulab/NAE).

Quantifying resilience of socio-ecological systems through dynamic Bayesian networks

Quantifying resilience of socio-ecological systems (SES) can be invaluable to delineate management strategies of natural resources and aid the resolution of socio-environmental conflicts. However, resilience is difficult to quantify and the factors contributing to it are often unknown. We provide a theoretical and conceptual framework to quantify resilience in a long-term context. Our approach uses elements from interdisciplinarity and network perspectives to establish links and causalities between social and ecological variables and resilience attributes. The evaluation and modeling of SES structure and function are established from the analysis of dynamic Bayesian networks (DBN). DBN models allow quantifying resilience through probabilities and offer a platform of interdisciplinary dialogue and an adaptive framework to address questions on ecosystem monitoring and management. The proposed DBN is tested in Monquentiva, a SES located in the high Andes of Colombia. We determined historical socio-ecological resilience from paleoecological evidence (palynological diversity, forest cover, fires, and precipitation) and social-economic factors (governance, social organization, and connectivity) between 1920 and 2019. We find that transformation processes in Monquentiva are mainly related to social change (e.g., social organization) and increased ecological diversity that in turn have fostered SES resilience between 1980 and 2019. The ability to predict the SES response over time and under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management. We conclude with a series of management and policy-relevant applications of the DBN approach for SES resilience assessment.

Reliability optimization of flexible test system based on pyro-mechanical device products production driven

The quality evaluation of aerospace pyro-mechanical device products(PDPs) is very important to the safety of aerospace engineering. Aerospace PDPs are small-lot batch, multiple batches and one-off products which rely on the analysis of ignition test with limited samples. To accommodate multiple batches and small-lot batch of aerospace PDPs, the flexible test system(FTS) switches between different batches of products by the selection of instruments and channels. The conventional test mode is driven by the completion time of batch products, which usually leads to frequent switching of instruments and channels and affects the reliability of test system. Therefore, the FTS is configured according to the completion of production missions within a certain time period in the future. This paper considers the FTS of aerospace PDPs as a phased mission system, and designs the corresponding dynamic Bayesian network(DBN) model. A test of one batch is taken as a phase of phased mission system. In order to improve efficiency and optimize reliability of the aerospace PDPs FTS, the receding horizon optimization problem is constructed. Numerical experiments show that the FTS with phased mission model can optimize the test sequence during the test process, which improves efficiency and reliability of test system.

Multistate time lag dynamic Bayesian networks model for reliability prediction of smart meters

Smart meters are the terminals of smart grids and have the characteristics of complex systems that units in the systems always have many tasks. The reliability prediction model of smart meters is less investigated and traditional models with the two-condition hypothesis are not applicable. In complex systems, multitasking unit has degradation characteristic. Failures of different tasks will affect other units with varying degrees and even lead to the failures of other units in advance. Hence, linear correlation analysis is unsuitable for failure correlation with time lags. Firstly, this paper proposes a two-parameter correlation estimated method for multistate units and multitasking units. The status data of units are divided into one-dimensional and two-state time series, cross-correlation coefficients and time migrations are obtained by analysing two-state time series and used to describe the time-lag correlation. Secondly, the Conditional Probability Table (CPT) of the Dynamic Bayesian Network (DBN) is updated by adding cross-correlation coefficients and time migrations, then time-lag Dynamic Bayesian Network (TDBN) is built. Additionally, the simulation analysis shows that prediction results of TDBN are more consistent of mean failure data. Lastly, the TDBN of the smart meter is built, and the effectiveness of TDBN is further proved by analysing the failure data of smart meters.

Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details

Dynamic Bayesian network (DBN) models are widely used for structural risk analysis because of their powerful parameter learning ability and capability of simplifying the problem by parsing it using nodes and arcs. However, these models update the reliability after inspection and maintenance (I&M) in a manner different than that specified by the aircraft structural integrity program (ASIP). In this study, a DBN model is developed to correctly represent the ASIP method. The model updates the crack length distribution after I&M based on the nondestructive testing (NDT) reliability and repair crack length distribution. The nodes for inequality and equality data are explicitly represented in the DBN, which correspond to the crack length vs. signal amplitude and noise characteristics of the NDT system. The proposed model overcomes drawbacks of existing models—initial overestimation and final underestimation—by appropriately considering the repair crack length distribution. Specifically, a decision node is used, which records the fraction of the crack length distribution removed after inspection. Furthermore, a method for constructing conditional probability tables is presented. The proposed model is applied to the in-service fatigue problem for a J85 engine compressor rotor blade. The findings demonstrate that the proposed model can be used in a wide range of applications.

A novel dynamic Bayesian network approach for data mining and survival data analysis

BackgroundCensorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan–Meier or restricted assumptions like the Cox regression model. On the other hand, Machine learning algorithms commonly rely on the high dimensionality of data and ignore the censorship attribute. In addition, these algorithms are more sophisticated to understand and utilize. We propose a novel approach based on the Bayesian network to address these issues.MethodsWe proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learned the structure of the directed acyclic graph. The likelihood approach conducted parameter learning. We conducted a simulation study to assess the performance of our model in comparison with the Kaplan–Meier and Cox proportional hazard regression. We defined various scenarios according to the sample size, censoring rate, and shapes of survival and censoring distributions across time. Finally, we fit the model on a real-world dataset that includes 760 post gastrectomy surgery due to gastric cancer. The validation of the model was explored using the hold-out technique based on the posterior classification error. Our survival model performance results were compared using the Kaplan–Meier and Cox proportional hazard models.ResultsThe simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan–Meier, especially in the late survival times. In the real-world data, the structure of the dynamic Bayesian network model satisfied the finding from Kaplan–Meier and Cox regression classical approaches. The posterior classification error found from the validation technique did not exceed 0.04, representing that our network predicted the state variables with more than 96% accuracy.ConclusionsOur proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightforward interpretation, handling of high-dimensional data, and few assumptions.

Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects.

Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the multiple uncertainty sources in the Paris crack propagation model, material property, and observation data of crack length. The uncertainty of monitoring data of vehicle loads is ignored because of its high accuracy; consequently, the vehicle-load-related uncertainty is spontaneously ignored, which is also demonstrated to be very small on the investigated actual bridges. Second, a hierarchical DBN model is introduced to construct mutual dependencies among various uncertainties and intra-correlations in the propagation process of multiple fatigue cracks at different components. Third, a stochastic traffic model is established based on the WIM monitoring data and multi-scale finite element analysis via substructure techniques to determine the probability distribution of vehicle-load-related parameters. After variable discretization, a refined exact inference algorithm in a forward–backward–forward manner is adopted to estimate the posterior distribution of equivalent initial crack length and update the established DBN model. Finally, the proposed method is demonstrated by a numerical case study and a typical application on an actual cable-stayed bridge with steel box girders using OSDs in China. The results show that the probability distribution of equivalent initial crack size can be spontaneously derived with a larger mean value than the results of conventional empirical analysis. Furthermore, the component-level fatigue reliability is tracked and predicted based on the established crack propagation model.

A Dynamic Bayesian Network Approach for Predicting Multitasking Performance

This study examines the utilities of a dynamic Bayesian network (DBN) to predict multitasking performance. Multitasking is the practice of conducting more than one task simultaneously. Compared with BN (Bayesian network), the DBN has the advantage of encoding both spatial and temporal relationships of the multiple variables under uncertain information. We established the DBN model based on contextual and observable variables from 19 participants to predict multitasking performance over time. The proposed DBN outperformed the BN model with smaller prediction errors and showed great potential to be used as a tool in error management and operator screening in diverse work environments.

Dynamic Bayesian Network for Predicting Tunnel-Collapse Risk in the Case of Incomplete Data

Collapse is one of the most dangerous aspects of drilling–blasting construction in highway tunnels. To accurately control tunnel-collapse risk, a multistate dynamic Bayesian network (DBN) evaluation method for highway tunnel collapse based on parameter learning was proposed. First, by analyzing the risk mechanism of tunnel construction, the initial BN model was established based on the causal relationship between risk factors and construction risk in hydrogeological conditions, construction technology, and construction management. Next, the construction process was discretized into finite time slices. In consideration of the fuzzy uncertainty of nodes, node polymorphism was introduced to construct a multistate DBN. Then, 50 typical tunnel-collapse cases were taken as sample data, and the conditional probability distribution of initial BN was derived using parameter learning based on the expectation-maximization (EM) algorithm. Using DBN reasoning and sensitivity analysis, the dynamic risk probability and the dominant factors of tunnel collapse were predicted. Finally, the DBN model was fed back with the measured cumulative values and velocity of the crown settlement, which updated the dynamic risk probability assessment results. In analyzing the collapse probability of Jinzhupa tunnel passing through the angular unconformity contact zone as an example, the results demonstrated that dynamic risk assessment results combined with monitoring data could better reflect the reality of construction contingencies, providing real-time risk management guidance.

Modelling and quantification of time-varying flood resilience for housing infrastructure using dynamic Bayesian Network

Natural hazards regularly cause severe damage to infrastructure systems. While these risks are highly unpredictable, the concept of resilience can be employed to make infrastructure more reliable, robust and recoverable. Infrastructure resilience is the capacity of an infrastructure system to revert to a desirable performance level following a disaster. As recovery—a time-dependent parameter—constitutes an element of resilience, this study quantifies time-varying housing infrastructure resilience against flood hazards using a dynamic Bayesian network (DBN). It develops a framework to quantify time-varying resilience and implements this framework in a real case-study area (Barak Valley, Northeast India). After selecting resilience parameters, this study develops two DBN models by considering two different approaches. Then, all of the information necessary for the various time-dependent resilience factors is collected through field surveys. The resilience values for different time periods are calculated using both approaches. Finally, this study compares the vulnerability, robustness and recovery scenarios of each area in the Barak Valley. This comparison, alongside the evaluated time-varying resilience values, will help public authorities provide resilience based decisions.

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model.

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.

A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring

Safety is of high societal concern in the petrochemical industry. With advancing digitalization, the techniques of probabilistic risk assessment (PRA), which provide a system-level perspective for industrial risk analysis, have become increasingly dynamic. This paper provides a further advancement in this direction by proposing a risk updating method based on the dynamic Bayesian network (DBN) to incorporate data monitoring into PRA in real-time. We update the probabilistic risk for basic events in Bayesian networks based on their prior probability and the probability of their online monitoring signals exceeding the alarm threshold. The key idea behind this approach is that if the signal of a basic event exceeds the alarm threshold, the corresponding risk should increase. Otherwise, the basic event should have a low risk. The contribution in this paper is twofold. First, residual methods are developed to estimate signal probabilities exceeding abnormal thresholds. Second, a DBN model is proposed to integrate prior risk with data monitoring for risk analysis. The proposed DBN model does not require additional expert knowledge and historical accident data to define the conditional relationship between data monitoring and prior risk. The proposed approach is validated using the RT 580 experimental setup and managed pressure drilling operations.