Safety Assessment Model Based on Dynamic Bayesian Network

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

According to the deficiency of traditional Markov chain approach in dynamic fault tree analysis, a new modular method for system reliability analysis is proposed. This paper focuses on dividing the fault tree of system into independent subtrees using a linear-time algorithm, and the processing method for different subtrees: Binary decision diagram solution for static subtrees and Bayesian Network solution for dynamic subtrees, respectively. In addition, an approach is also provided for mapping some dynamic logic gates into discrete-time Bayesian network. At last, the modular method has been applied to assess the reliability of a satellite key device and the results have shown that the proposed method can overcome the state explosion problem, and is useful for assessing the reliability of large and complex systems.

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Considering the dynamic characteristics of Integrated Modular Avionics (IMA) core processing system, such as fault-tolerant redundancy and sequence correlation, it is not enough to simply determine the results of different combinations of fault events, it is also necessary to understand the correlation of their failures. The Dynamic Fault Tree (DFT) analysis method is applied to the safety design of the IMA core processing system to ensure that the system design meets the safety requirements of aircraft. However, Dynamic Fault Tree Analysis (DFTA) requires a lot of manual effort, it will cause the state space explosion and low calculation accuracy, which makes it time-consuming and expensive. This paper presents a dynamic fault tree analysis based on Discrete-Time Bayesian Network (DTBN). Aimed at verifying the characteristics of the presented approach, we conduct a comparative experiment on the accuracy of calculating top events probability under DTBN method and Markov chain analysis method. Finally, we draw the conclusion that the DTBN method has higher accuracy and can effectively avoid space-and-state explosion at the same time.

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a dynamic fault tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a dynamic Bayesian network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFT exploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S.Cerevisiae cell cycle gene expression data. The experimental results demonstrate that the accuracy of our method outperforms the previous work

Real-world entities comprising a complex system evolve as a function of time and respond to external perturbations. Dynamic Bayesian networks extend the fundamental ideas behind static Bayesian networks to model associations arising from the temporal dynamics between the entities of interest. This has to be contrasted with static Bayesian networks, which model the network structure from multiple independent realizations of the entities of a snapshot of the process. More importantly, incorporating the temporal signatures is useful in capturing possible feedback loops that are implicitly disregarded in the case of static Bayesian networks. Since feedback loops are ubiquitous in biological pathways, dynamic Bayesian network modeling is expected to result in better representations of such pathways.

Background. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting upper and lower motor neurons in the brain and spinal cord. The heterogeneity in the course of ALS clinical progression and ultimately survival, coupled with the rarity of this disease, make predicting disease outcome at the level of the individual patient very challenging. Besides, stratification of ALS patients has been known for years as a question of great importance to clinical practice, research and drug development. Methods. In this work, we present a Dynamic Bayesian Network (DBN) model of ALS progression to detect probabilistic relationships among variables included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), which provides records of over 10,700 patients from different clinical trials, and with over 2,869,973 longitudinally collected data measurements. Results. Our model unravels new dependencies among clinical variables in relation to ALS progression, such as the influence of basophil count and creatine kinase on patients’ clinical status and the respiratory functional state, respectively. Furthermore, it provided an indication of ALS temporal evolution, in terms of the most probable disease trajectories across time at the level of both patient population and individual patient. Conclusions. The risk factors identified by out DBN model could allow patients' stratification based on velocity of disease progression and a sensitivity analysis on this latter in response to changes in input variables, i.e. variables measured at diagnosis.

Tracing and prediction analysis of an urban pipeline leakage accident based on the catastrophe DBN model

In order to find out the problems of students' learning in the online learning process in time and ensure that students can successfully complete the online learning course, the dynamic Bayesian network is used to predict students' online learning scores. First, the online course Bayesian network is established, and then the dynamic Bayesian network is established by combining the Bayesian network and hidden Markov according to the time slice. The maximum expectation algorithm is used for Bayesian parameter learning, and the scores are predicted according to video learning, chapter test and other influencing factors. The results show that the dynamic Bayesian network can effectively predict the online learning results, the prediction results provide teachers with decision-making, and students can carry out targeted reinforcement learning according to the prediction results in the learning process.

Bayesian network based dynamic operational risk assessment

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

In this article, we develop and evaluate three Dynamic Bayesian Network (DBN) models for assessing temporally variable learner scientific inquiry skills (Hypothesis Generation and Variable Identification) in INQPRO learning environment. Empirical studies were carried out to examine the matching accuracies and identify the models’ drawbacks. We demonstrate how the insights gained from a preceding model have eventually led to the improvement of subsequent models. In this study, the entire evaluation process involved 6 domain experts and 61 human learners. The matching accuracies of the models are measured by (1) comparing with the results gathered from the pretest, posttest, and learner’s self-rating scores; and (2) comments given by domain experts based on learners’ interaction logs and the graph patterns exhibited by the models.KeywordsScientific Inquiry SkillsDynamic Bayesian Networks

Fire probability prediction of offshore platform based on Dynamic Bayesian Network

In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.