Equipment Impact Rate Assessment Using Bayesian Networks

2016 ◽  
Author(s):  
Smitha D. Koduru ◽  
Dongliang Lu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Common Ground ◽  
Fault Tree ◽  
Prior Probabilities ◽  
Tree Methods ◽  
Reporting Tool ◽  
Triggering Events ◽  
Intuitive Method ◽  
Tree Method

Bayesian networks offer an intuitive method of modelling causal relationships between the triggering events that lead to equipment impact on a pipeline. This method offers an advantage over the more well-known fault-tree methods due to its ability to use Bayesian inference for updating the prior probabilities of triggering events that lead to equipment impact such as, failure of permanent markers, use of one-call system, or failure of right-of-way patrol. In this paper, a modelling approach for a Bayesian network for equipment impact assessment, based on the available fault-tree method, is demonstrated. The advantages of the Bayesian network, such as updating the occurrence rates of basic triggering events and tracking information flow based on partial and incomplete information are illustrated by using the event data available from the damage incident reporting tool (DIRT) of Common Ground Alliance (CGA).

Application of Bayesian Network in Safety Evaluation of Metro Construction

2013 ◽  
Vol 838-841 ◽  
pp. 1463-1468
Author(s):  
Xiang Ke Liu ◽  
Zhi Shen Wang ◽  
Hai Liang Wang ◽  
Jun Tao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Posterior Probability ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Safety Evaluation ◽  
Tree Analysis ◽  
Tunnel Blasting ◽  
Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

scholarly journals Determination of the Type of Heart Syndrome in Traditional Chinese Medicine with the Bayesian Network Method

2020 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Asto Buditjahjanto
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Prior Probabilities ◽  
Network Method

The determination of a disease syndrome in the TCM is difficult enough to determine because it requires a lot of experience in observing patients' symptoms that appear in disease syndrome and their disease syndrome history. Symptoms that appear in one disease syndrome are varied and can also appear in other disease syndromes. This research limits the determination of the type of syndrome only in the heart organ. The purpose of this study is to determine the type of heart syndrome in TCM by using Bayesian Networks. Bayesian Networks is used because it has the advantage of adapting expert knowledge toward the preferences of symptoms that arise at a type of heart syndrome. The expert's preference is in the weights that act as prior probabilities that are used as the basis for calculations on the Bayesian Networks. The results showed that the Bayesian Networks can be used to determine the type of heart syndrome well. The results of trials on 7 patients yield the same diagnosis between the doctor's diagnosis and the Bayesian Networks calculation

scholarly journals Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuqing Bi ◽  
Chenming Li ◽  
Xujie Li ◽  
Hongmin Gao
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Historical Data ◽  
Complex Structure ◽  
Fault Tree ◽  
Uncertain Information ◽  
New Approach ◽  
Pumping Station ◽  
The One ◽  
Tree Method

According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

scholarly journals Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

2021 ◽  
Vol 19 (1) ◽  
pp. 297
Author(s):  
Yang Liu ◽  
Xiaoxue Ma ◽  
Weiliang Qiao ◽  
Huiwen Luo ◽  
Peilong He
Keyword(s):  
Human Factors ◽  
Bayesian Network ◽  
Human Factor ◽  
Fuzzy Theory ◽  
Fault Tree ◽  
Expert Elicitation ◽  
Risk Modeling ◽  
Nested Model ◽  
Prior Probabilities ◽  
Operational Activities

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

System Reliability Evaluation Using Bayesian Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2457-2461
Author(s):  
Lin Ying Liu ◽  
Qin Sun ◽  
Yao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Network Inference ◽  
Fault Tree ◽  
Reliability Evaluation ◽  
Flow Chart ◽  
Inference Algorithms ◽  
Network Method ◽  
Bayesian Network Inference

Bayesian network method for system reliability evaluation which is based on a Bayesian network that transformed from a fault tree has gotten much attention these years. After a brief introduction to the method how to transform a fault tree into a Bayesian network, the paper elaborates the Bayesian network inference algorithms. The paper focuses on the way how the inference algorithms can be applied to the practice of system reliability evaluation and designs a systematic flow chart used to evaluate system reliability in a Bayesian network way. The experiment demonstrates the feasibility of the systematic flow chart.

scholarly journals Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

2021 ◽  
Vol 53 (5) ◽  
pp. 210509
Author(s):  
Zhenliang Fu ◽  
Na Li ◽  
Xueyan Tian ◽  
Yonghua Li ◽  
Ziqiang Sheng
Keyword(s):  
Missing Data ◽  
Bayesian Network ◽  
Reliability Analysis ◽  
Fuzzy Theory ◽  
Probability Model ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Practical Experience ◽  
Prior Probabilities ◽  
System A

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

scholarly journals City Fire Risk Analysis based on Coupling Fault Tree Method and Triangle Fuzzy Theory

2014 ◽  
Vol 84 ◽  
pp. 204-212 ◽  
Author(s):  
Wu Aiyou ◽  
Shi Shiliang ◽  
Li Runqiu ◽  
Tang Deming ◽  
Tang Xiafang
Keyword(s):  
Risk Analysis ◽  
Fuzzy Theory ◽  
Fault Tree ◽  
Fire Risk ◽  
Fire Risk Analysis ◽  
Coupling Fault ◽  
Tree Method
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