Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network
In order to evaluate the risk level of the urban gas pipeline system, and provide the reference for follow-up prevention efforts, a quantitative analysis method of gas pipeline accident risk was proposed based on polymorphic fuzzy Bayesian network. Firstly, risk factors were sorted out from 86 accid...
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| Format: | Article |
| Language: | zho |
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Hebei University of Science and Technology
2023-08-01
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| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304010?st=article_issue |
| _version_ | 1797435656168800256 |
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| author | Ying QU Xuming WANG Yuheng WANG Jingyi ZHANG |
| author_facet | Ying QU Xuming WANG Yuheng WANG Jingyi ZHANG |
| author_sort | Ying QU |
| collection | DOAJ |
| description | In order to evaluate the risk level of the urban gas pipeline system, and provide the reference for follow-up prevention efforts, a quantitative analysis method of gas pipeline accident risk was proposed based on polymorphic fuzzy Bayesian network. Firstly, risk factors were sorted out from 86 accident investigation reports, so that the city gas pipeline risk element system was established. Subsequently, the fault tree model was built to seek the match between risk hazards and accidents, which can convert into the Bayesian network structure. After that, fuzzy set theory and probability distribution method were introduced to calculate the prior probability of the root node and the conditional probability of the intermediate nodes, evidence-based inference of Bayesian network was used to predict the probability of accidents, analyze the importance of risk elements, and reverse diagnose key causal factors. Finally, this method was applied to the risk analysis of the “10·21” large pipeline gas leakage accident in Shenyang. The results of the case validation show the a priori probability of the accident is 688%, which verifies the effectiveness of the risk system. Besides, important risk elements derived from prediction and backward diagnosis are consistent with the direct causes analyzed in the accident investigation report. The polymorphic fuzzy Bayesian network approach for gas pipeline system risk can evaluate gas pipeline accident risk accurately and identify key risk-causing factors, which provides some reference for decision making in the safety management of city gas pipelines. |
| first_indexed | 2024-03-09T10:50:21Z |
| format | Article |
| id | doaj.art-5fa42788bef34b0ea646a2ecf60e948a |
| institution | Directory Open Access Journal |
| issn | 1008-1542 |
| language | zho |
| last_indexed | 2024-03-09T10:50:21Z |
| publishDate | 2023-08-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj.art-5fa42788bef34b0ea646a2ecf60e948a2023-12-01T07:27:14ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422023-08-0144441142010.7535/hbkd.2023yx04010b202304010Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian networkYing QU0Xuming WANG1Yuheng WANG2Jingyi ZHANG3Data Science and Intelligent Computing Research Center, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaData Science and Intelligent Computing Research Center, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Social Sciences, Mokwon University, Daejeon Gwangyeoks 340-934, KoreaIn order to evaluate the risk level of the urban gas pipeline system, and provide the reference for follow-up prevention efforts, a quantitative analysis method of gas pipeline accident risk was proposed based on polymorphic fuzzy Bayesian network. Firstly, risk factors were sorted out from 86 accident investigation reports, so that the city gas pipeline risk element system was established. Subsequently, the fault tree model was built to seek the match between risk hazards and accidents, which can convert into the Bayesian network structure. After that, fuzzy set theory and probability distribution method were introduced to calculate the prior probability of the root node and the conditional probability of the intermediate nodes, evidence-based inference of Bayesian network was used to predict the probability of accidents, analyze the importance of risk elements, and reverse diagnose key causal factors. Finally, this method was applied to the risk analysis of the “10·21” large pipeline gas leakage accident in Shenyang. The results of the case validation show the a priori probability of the accident is 688%, which verifies the effectiveness of the risk system. Besides, important risk elements derived from prediction and backward diagnosis are consistent with the direct causes analyzed in the accident investigation report. The polymorphic fuzzy Bayesian network approach for gas pipeline system risk can evaluate gas pipeline accident risk accurately and identify key risk-causing factors, which provides some reference for decision making in the safety management of city gas pipelines.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304010?st=article_issuerisk evaluation and failure analysis; gas piping system; fuzzy theory; polymorphic fuzzy bayesian networks; probability distribution |
| spellingShingle | Ying QU Xuming WANG Yuheng WANG Jingyi ZHANG Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network Journal of Hebei University of Science and Technology risk evaluation and failure analysis; gas piping system; fuzzy theory; polymorphic fuzzy bayesian networks; probability distribution |
| title | Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network |
| title_full | Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network |
| title_fullStr | Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network |
| title_full_unstemmed | Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network |
| title_short | Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network |
| title_sort | risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy bayesian network |
| topic | risk evaluation and failure analysis; gas piping system; fuzzy theory; polymorphic fuzzy bayesian networks; probability distribution |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304010?st=article_issue |
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