IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v232y2023ics0951832022006998.html
   My bibliography  Save this article

Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method

Author

Listed:
  • Chen, Yinuo
  • Tian, Zhigang
  • He, Rui
  • Wang, Yifei
  • Xie, Shuyi

Abstract

Gas transmission stations (GTS) are significant infrastructure for cities and critical components of natural gas delivery, with severe consequences in the case of an accident. As a result, it necessitates the importance of potential risk discovery and accident precursor identification. However, existing models for risk analysis of GTS systems are too complex and only periodically update the risk of GTS, making it difficult to discover its potential risk in time. Some data used as input to the models are not from the system under consideration, leading to results inconsistent with the actual working conditions. This study proposes a structure mapping method based on failure modes and effects analysis (FMEA) to form the GTS's object-oriented Bayesian network (OOBN) framework, making the model more user-friendly. An accident precursor identification approach is proposed based on the piecewise aggregate approximation-cumulative sum (PAA-CUSUM) algorithm, which can better discover the potential risks in real-time. The proposed method identifies process anomalies through monitoring data and analyzes the events and propagation patterns with the highest potential risk. A case study of a GTS in China is conducted. The results demonstrate that the proposed method is beneficial for assisting station operators in identifying possible hazards and providing a foundation for daily risk mitigation.

Suggested Citation

  • Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006998
    DOI: 10.1016/j.ress.2022.109084
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022006998
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.109084?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Lee, Dooyoul & Choi, Dongsu, 2020. "Analysis of the reliability of a starter-generator using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    3. Marta Regina Cezar-Vaz & Laurelize Pereira Rocha & Clarice Alves Bonow & Mara Regina Santos Da Silva & Joana Cezar Vaz & Letícia Silveira Cardoso, 2012. "Risk Perception and Occupational Accidents: A Study of Gas Station Workers in Southern Brazil," IJERPH, MDPI, vol. 9(7), pages 1-16, July.
    4. Xie, Shuyi & Dong, Shaohua & Chen, Yinuo & Peng, Yujie & Li, Xincai, 2021. "A novel risk evaluation method for fire and explosion accidents in oil depots using bow-tie analysis and risk matrix analysis method based on cloud model theory," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Huang, Wencheng & Kou, Xingyi & Zhang, Yue & Mi, Rongwei & Yin, Dezhi & Xiao, Wei & Liu, Zhanru, 2021. "Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    8. Raman MR, Gauthama & Somu, Nivethitha & Mathur, A.P., 2020. "A multilayer perceptron model for anomaly detection in water treatment plants," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    9. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    10. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2019. "An integrated approach for real-time hazard mitigation in complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 297-309.
    11. Skogdalen, Jon Espen & Vinnem, Jan Erik, 2012. "Combining precursor incidents investigations and QRA in oil and gas industry," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 48-58.
    12. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
    13. Chi, Chia-Fen & Sigmund, Davin & Astardi, Martin Octavianus, 2020. "Classification Scheme for Root Cause and Failure Modes and Effects Analysis (FMEA) of Passenger Vehicle Recalls," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    14. Saleh, Joseph H. & Saltmarsh, Elizabeth A. & Favarò, Francesca M. & Brevault, Loïc, 2013. "Accident precursors, near misses, and warning signs: Critical review and formal definitions within the framework of Discrete Event Systems," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 148-154.
    15. Haller, Piroska & Genge, Béla & Duka, Adrian-Vasile, 2019. "On the practical integration of anomaly detection techniques in industrial control applications," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 48-68.
    16. Jones, B. & Jenkinson, I. & Yang, Z. & Wang, J., 2010. "The use of Bayesian network modelling for maintenance planning in a manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 267-277.
    17. van Staalduinen, Mark Adrian & Khan, Faisal & Gadag, Veeresh & Reniers, Genserik, 2017. "Functional quantitative security risk analysis (QSRA) to assist in protecting critical process infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 23-34.
    18. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    19. Li, Zan & Wang, Fengming & Wang, Chengjie & Hu, Qingpei & Yu, Dan, 2021. "Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    20. Babaleye, Ahmed O. & Kurt, Rafet Emek & Khan, Faisal, 2019. "Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 133-141.
    21. Chang, Yuanjiang & Wu, Xiangfei & Zhang, Changshuai & Chen, Guoming & Liu, Xiuquan & Li, Jiayi & Cai, Baoping & Xu, Liangbin, 2019. "Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 454-462.
    22. Peeters, J.F.W. & Basten, R.J.I. & Tinga, T., 2018. "Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 36-44.
    23. Wang, Qun & Jia, Guozhu & Jia, Yuning & Song, Wenyan, 2021. "A new approach for risk assessment of failure modes considering risk interaction and propagation effects," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    24. Xu, Sheng & Kim, Ekaterina & Haugen, Stein & Zhang, Mingyang, 2022. "A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    25. Liu, Shuanglei & Li, Weijun & Gao, Peng & Sun, Yibo, 2022. "Modeling and performance analysis of gas leakage emergency disposal process in gas transmission station based on Stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    26. Wu, Zhang & Yang, Mei & Jiang, Wei & Khoo, Michael B.C., 2008. "Optimization designs of the combined Shewhart-CUSUM control charts," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 496-506, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Guoqi & Pu, Gang & Yang, Jiaxin & Jiang, Xinguo, 2024. "A multidimensional quantitative risk assessment framework for dense areas of stay points for urban HazMat vehicles," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Wang, Xinjian & Xia, Guoqing & Zhao, Jian & Wang, Jin & Yang, Zaili & Loughney, Sean & Fang, Siming & Zhang, Shukai & Xing, Yongheng & Liu, Zhengjiang, 2023. "A novel method for the risk assessment of human evacuation from cruise ships in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Zhiqiang Liu & Wenbo Zhu & Hongzhou Zhang & Shengjin Wang & Lu Fang & Weijun Hong & Hua Shao & Guopeng Wang, 2020. "Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
    4. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Fam, Mei Ling & He, Xuhong & Konovessis, Dimitrios & Ong, Lin Seng, 2020. "Using Dynamic Bayesian Belief Network for analysing well decommissioning failures and long-term monitoring of decommissioned wells," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    6. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Bhattacharjee, Pushparenu & Dey, Vidyut & Mandal, U.K. & Paul, Susmita, 2022. "Quantitative risk assessment of submersible pump components using Interval number-based Multinomial Logistic Regression(MLR) model," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Chuan Wang & Jun Gou & Yingcheng Tian & Hao Jin & Chao Yu & Yupeng Liu & Jiajun Ma & Yong Xia, 2022. "Reliability and availability evaluation of subsea high integrity pressure protection system using stochastic Petri net," Journal of Risk and Reliability, , vol. 236(3), pages 508-521, June.
    12. Xue, Gang & Liu, Shifeng & Ren, Long & Gong, Daqing, 2024. "Risk assessment of utility tunnels through risk interaction-based deep learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    13. Ding, Rui & Liu, Zehua & Xu, Jintao & Meng, Fanpeng & Sui, Yang & Men, Xinhong, 2021. "A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    15. Hai, Nan & Gong, Daqing & Liu, Shifeng & Dai, Zixuan, 2022. "Dynamic coupling risk assessment model of utility tunnels based on multimethod fusion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    17. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    18. Di Zhang & Xinping Yan & Zaili Yang & Jin Wang, 2014. "An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case," Journal of Risk and Reliability, , vol. 228(2), pages 176-188, April.
    19. Zhou, Jing & Liu, Yu & Liang, Decui & Tang, Maochun, 2023. "A new risk analysis approach to seek best production action during new product introduction," International Journal of Production Economics, Elsevier, vol. 262(C).
    20. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006998. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.