Dynamic Bayesian Network Method Research Articles

Risk analysis and assessment of underwater glider mission failure using Dynamic Bayesian Network method

The Autonomous Underwater Glider (AUG), with its extended operational endurance, encounters various uncertain risks during diverse mission tasks. Therefore, conducting dynamic risk analysis is crucial for implementing subsequent decisions and improving equipment safety. This paper combines Fault Tree Analysis (FTA) and Dynamic Bayesian Network (DBN) to develop a customized dynamic risk assessment method for AUG task failure under multiple risk factors. Firstly, preliminary risk factors are identified by FTA using existing experimental data. Next, transformation rules are constructed to map FTA into a basic DBN model. Then, parameters such as conditional probabilities are determined through logic gates and parameter learning, and the proposed model is validated based on axioms. Finally, key risk factors are identified based on actual failure data. The research results indicate that the key risk factors leading to AUG task failure are progressive abnormalities such as biofouling and ocean current disturbance. The method proposed in this paper can achieve dynamic risk assessment, providing strong references for operators in making decisions for subsequent tasks.

Developing a bi-objective maintenance optimization model for process industries by prioritizing resilience and robustness using dynamic Bayesian networks

In this paper, we developed a new model that combines resilience concept with robust maintenance resource allocation, and team assignment strategies to evaluate system performance after disruption. This model offers insights for proactive preparation before disruptive events and facilitates optimal decision-making in the aftermath of such disruptions. Our approach involves a non-linear programming model that operates at both strategic and operational decision levels. It aims to simultaneously minimize total maintenance costs while maximizing resilience by determining the allocation of backup resources to system components before a disruption occurs and suggesting emergency maintenance team assignments post-disruption, all while adhering to resilience constraints.We quantify resilience using a dynamic Bayesian Network method and Markov process, utilizing metrics based on absorption, adaptation, and restoration performance (reliability) curves. We propose a bi-objective optimization model and leverage the genetic algorithm NSGA-III to obtain Pareto-optimal solutions. To address the inherent uncertainty of various disruption scenarios, we generate a range of possible scenarios and employ robust optimization techniques to derive solutions that can withstand these uncertainties. To demonstrate the effectiveness of our proposed model, we conduct a case study involving a City Gate Station in agas industry.

A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method

The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method.

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 reconfigurable dynamic Bayesian network for digital twin modeling of structures with multiple damage modes

Dynamic Bayesian networks (DBNs) are commonly employed for structural digital twin modeling. At present, most researches only consider single damage mode tracking. It is not sufficient for a reusable spacecraft as various damage modes may occur during its service life. A reconfigurable DBN method is proposed in this paper. The structure of the DBN can be updated dynamically to describe the interactions between different damages. Two common damages (fatigue and bolt loosening) for a spacecraft structure are considered in a numerical example. The results show that the reconfigurable DBN can accurately predict the acceleration phenomenon of crack growth caused by bolt loosening while the DBN with time-invariant structure cannot, even with enough updates. The definition of interaction coefficients makes the reconfigurable DBN easy to track multiple damages and be extended to more complex problems. The method also has a good physical interpretability as the reconfiguration of DBN corresponds to a specific mechanism. Satisfactory predictions do not require precise knowledge of reconfiguration conditions, making the method more practical.

A general robust dynamic Bayesian network method for supply chain disruption risk estimation under ripple effect

Robust dynamic Bayesian network (DBN) is a valid tool for disruption propagation estimation in the supply chain under data scarcity. However, one of assumptions in robust DBN is that the Markov transition matrix is fixed and fully known, which is unpractical. To make up this deficiency, a novel and general robust DBN is, for the first time, proposed in this work to assess the worst-case oriented supply chain disruption risk under ripple effect. The study focuses on a supply chain with multiple suppliers and one manufacturer over a time horizon, in which only probability intervals of related probabilities are known. The objective is to obtain the worst-case supply chain disruption risk, measured by the probability of the manufacturer in the fully disrupted state in the final time period. For the problem, a new and general nonconvex programming model is established. Then, a case study is conducted to compare our approach with the classic DBN and robust DBN in the literature.

Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

Safety barriers play a critical role in preventing unintentional hydrocarbon flow leaking from reservoir to external environment or another formation during different offshore operation stages, and such a leakage has the potential to trigger cascading events and may lead to catastrophic consequences. The present study aims at the development of a hybrid DBN-based approach for dynamic performance analysis of safety barriers in the prevention of subsea downhole leakage incidents. Events in operation, such as different types of maintenances and process demand are taken into account to enhance the safety barrier performance. These factors could be analyzed by reflecting inspecting and repair activities of safety barriers with multistate-based multiphase Markov process. In order to obtain a dynamic and synthetic risk analysis of subsea downhole leakage, a dynamic Bayesian network-based model is proposed, incorporating the failure analysis of safety barriers and downhole multiple leakage pathways. Such analysis allows determining the dynamic risk characteristic of leakage events, and key safety barriers under different maintenance scenarios. Dynamic performance of such safety barriers is evaluated with respect to four aspects: preventive maintenance and imperfect repair, degradation effects, process demand and maintenance cost. The approach is tested through the application to a case study with an offshore oil and gas well. The results the importance of safety barrier performance in controlling the expected leakage scenarios.

The Dynamic Relationship between Macroeconomy and Stock Market in China: Evidence from Bayesian Network

As the most important component of the capital market, the stock market has always been regarded as the “barometer” of the macroeconomy. However, many researchers have found that the stock market and macroeconomy are operating separately. This paper uses the dynamic Bayesian network method to study the dynamic relationship between the Chinese macroeconomic system and the stock market. The study found that the correlation between the macroeconomic system and the stock market is not consistent in different time periods. For most of the time, the stock system and the macroeconomic system are relatively independent. However, several macroeconomic factors such as Purchase Management Index could affect the stock market through some industries. A conclusion is drawn that the “barometer” function of the stock market is weak and easy to be damaged by factors such as the irrational sentiment of investors.

Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products

The flexible test system (FTS) for aerospace pyromechanical device products (PDPs) is an important mean to accurately assess the quality of aerospace-grade PDPs. Since PDPs are disposable products produced in small batch and multi-batch, only a limited number of samples can be tested for each batch to evaluate the quality level of whole batch of products. Therefore, the reliability of ignition FTS is concerned to ensure the reliability of limited test samples. To meet the test requirements of PDPs, the ignition FTS adopts a flexible method to adapt to the different batch types and models. Besides, the reliability of ignition FTS is related to time and product batch task, the reliability evaluation method based on traditional Bayesian network can't describe the dynamic process of product batch. Therefore, this paper proposes a dynamic Bayesian network method based on the combination of time and batch factors to overcome the limitations of product batch change, and a channel configuration optimization method is introduced to meet the reliability requirements, which provides an effective mean for evaluating the FTS reliability. Numerical experiments show that the method can accurately reflect the reliability change of FTS for aerospace PDPs.

Dynamic analysis for fire-induced domino effects in chemical process industries

Domino effects are typically high-impact low-probability (HILP) accidents, which pose a serious threat to chemical process industries. Previous researches on domino effects in chemical industries focus more on static analysis at the spatial scale. From the perspective of the spatial and temporal characteristics of the accident, this study proposed a model to analyze dynamic evolution process of domino effects by using matrix calculation coupled with Monte Carlo simulation, and the dynamic propagation of pool fire accidents is considered as the evolution of domino effects. The algorithm of the model for dynamic domino probabilities considering the synergistic effects of multiple escalation vectors from different units can be used to analyze the complex scenarios of domino effects with high-level and multiple primary accident units. Moreover, the model can be applied in chemical areas with a large number of installations due to the greatly improved calculation efficiency. The proposed model is tested and validated using earlier studied dynamic Bayesian network method, and the application of the model is demonstrated on a complex multi-unit system. The results show that domino effects have strong temporal correlation, and the scenario with multiple primary accident units is much more serious than that with only one primary accident unit, which provide important support for the implementation of emergency response. The study highlights that the proposed model serves as an important tool to evaluate strategies for prevention and control of domino effects.

A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles

This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.

Stability-based Dynamic Bayesian Network method for dynamic data mining

In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model.

Enhancing the Prediction Performance of Real-Time Crash Prediction Models: A Cell Transmission-Dynamic Bayesian Network Approach

Predicting crash probability in real time is a concept that has inspired studies into complex modeling methods, exploring more sophisticated data collection methods, reducing specification errors by introducing new traffic variables, etc. Most existing real-time crash prediction models (RTCPMs) are based on loop detector data and the model architecture, and their prediction performances are sensitive to the location and layout of detectors with respect to crashes, that is, the spacing of detectors on the road section. Different expressways/freeways have different detector layouts and these vary substantially even within the same expressway/freeway. This limitation is a major obstacle in developing a universal RTCPM. To address this, this paper takes one-minute aggregated loop detector data along with detector layouts as input and employs a cell transmission model (CTM) to interpolate states of traffic flow variables for a pre-defined hypothetical detector layout. Next, several RTCPMs are constructed using Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) for existing and CTM-based detector layouts. It is observed that the CTM can generate traffic flow parameters with a mean percentage error of 12.96%. The results further suggest that the model constructed with the CTM-based uniformly spaced simulated detector data coupled with the DBN method performed better than the model based on the BN and existing non-uniform detector layout.

Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network

Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.

Fractional Vegetation Cover Estimation Method Through Dynamic Bayesian Network Combining Radiative Transfer Model and Crop Growth Model

Fractional vegetation cover (FVC) is an important parameter for describing the conditions of land surface vegetation and is widely used for Earth surface process simulations and global change studies. Regional FVC is primarily derived from remotely sensed data. However, current FVC estimation methods are mainly employed on remotely sensed data at a single time point, which can only reflect the instantaneous physical state of the land surface and ignore the important information from the vegetation growing characteristics. The vegetation growing characteristics have great potential to capture the temporal variations of FVC and, thus, can provide complementary information to improve the FVC estimation accuracy. In this paper, a dynamic Bayesian network method was proposed to estimate FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data through combining a radiative transfer model and a statistical crop growth model, which could synthetically use information from both remote sensing data and crop growing characteristics. The performance of the proposed method was investigated in a cropland area of the Heihe River Basin, covering the whole growing season of maize in 2012. The time series field FVC data were quantitatively measured using digital photography and then used to generate high-spatial-resolution FVC maps using the Advanced Spaceborne Thermal Emission and Reflection Radiometer and Compact Airborne Imaging Spectrometer data for evaluating the accuracy of FVC estimates from MODIS data. The validation results showed a satisfactory performance with a coefficient of determination $R^{2}$ of 0.956 and a root-mean-square error (RMSE) of 0.057, as compared with the performance ( $R^{2} = 0.817$ , $\text{RMSE} = 0.106$ ) of the FVC estimates using the lookup table method, which utilized the information from remote sensing data. These results indicated that the proposed method could effectively utilize the vegetation growth information and achieve reliable FVC estimates in the cropland area.

On Pass-through of RMB Exchange Rate to Prices of Different Industries

Abstract This paper employs time series method and Dynamic Bayesian Network method to measure the pass through of RMB exchange rate to prices of different industries. The result shows that the direct transfer effect of RMB exchange rate on RMPI, CGPI and CPI is remarkable. At the same time, the RMB exchange rate can also have indirect influence on PPI, CGPI and CPI of different industries. This study indicates that wood, chemistry, textile and other labor-intensive industry prices are sensitive to the fluctuation of RMB exchange rate, while the exchange rate pass-through effects of energy, black metal, non-ferrous metals and other mineral industries prices are not significant.

Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network

Remotely sensed data is the main source of vegetation leaf area index (LAI) information on the regional to global scale. Many validation results have revealed that the accuracy of the retrieved LAI is often affected by the cloud cover of imagery, instrument problems, and inversion algorithms. Ground meteorological station data, characterized by relatively high accuracy and time continuity compared with remote sensing data, can provide complementary information to remote sensing observations. In this paper, we combine the potential advantages of both types of data in order to improve LAI retrievals in the Heihe River Basin, an arid and semi-arid area in northwest China where Moderate Resolution Imaging Spectroradiometer (MODIS) LAI values are significantly underestimated. A dynamic Bayesian network (DBN) is used to integrate these two data types for time series LAI estimation. Results show that the square of correlation coefficient between LAI values estimated by our DBN method (referred to as DBN LAI) and field measured LAI values is 0.76, with a root mean square error of 0.78. The DBN LAI are closer to field measurements than the MODIS LAI standard product values. Moreover, by introducing ground meteorological station data using a dynamic process model, DBN LAI show better temporal consistency than the MODIS LAI. It is concluded that the quality of LAI retrievals can be improved by combining remote sensing data and ground meteorological station data using a filtering inference algorithm in a DBN framework. More importantly, the study provides a basis and method for utilizing ground meteorological station network data to estimate land surface parameters on a regional scale.

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.

A conditional random fields method for RNA sequence–structure relationship modeling and conformation sampling

Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence–structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence–structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.Contact: zywang@ttic.edu; j3xu@ttic.eduSupplementary Information: Supplementary data are available at Bioinformatics online.

Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

BackgroundDynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli.ResultsIn this paper we investigate a novel non-stationary DBNs method with a potential regulator detection technique and a flexible lag choosing mechanism. We apply the approach for the gene regulatory network inference on three non-stationary time series data. For the Macrophages and Arabidopsis data sets with the reference networks, our method shows better network structure prediction accuracy. For the Drosophila data set, our approach converges faster and shows a better prediction accuracy on transition times. In addition, our reconstructed regulatory networks on the Drosophila data not only share a lot of similarities with the predictions of the work of other researchers but also provide many new structural information for further investigation.ConclusionsCompared with recent proposed non-stationary DBNs methods, our approach has better structure prediction accuracy By detecting potential regulators, our method reduces the size of the search space, hence may speed up the convergence of MCMC sampling.