IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p1109-d1371484.html
   My bibliography  Save this article

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

Author

Listed:
  • Longlong He

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Ruiyu Pan

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Yafei Wang

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Jiani Gao

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Tianze Xu

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Naqi Zhang

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Yue Wu

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

  • Xuhui Zhang

    (Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an University of Science and Technology, Xi’an 710049, China)

Abstract

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Suggested Citation

  • Longlong He & Ruiyu Pan & Yafei Wang & Jiani Gao & Tianze Xu & Naqi Zhang & Yue Wu & Xuhui Zhang, 2024. "A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process," Mathematics, MDPI, vol. 12(7), pages 1-31, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1109-:d:1371484
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/1109/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/7/1109/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Zhang, Yan & Wang, Si-Xia & Yao, Jian-Ting & Tong, Rui-Peng, 2023. "The impact of behavior safety management system on coal mine work safety: A system dynamics model of quadripartite evolutionary game," Resources Policy, Elsevier, vol. 82(C).
    3. Binay Prakash Pandey & Devi Prasad Mishra, 2023. "Developing an Alternate Mineral Transportation System by Evaluating Risk of Truck Accidents in the Mining Industry—A Critical Fuzzy DEMATEL Approach," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    4. Xianzhong Li & Shigang Hao & Tao Wu & Weilong Zhou & Jinhao Zhang, 2023. "Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    5. Jimei Li & Yunhui Wang & An Chen & Guanghui Wang & Xiaohui Yao & Tongtong Wang, 2023. "Construction and empirical testing of comprehensive risk evaluation methods from a multi-dimensional risk matrix perspective: taking specific types of natural disasters risk in China as an example," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(2), pages 1245-1271, June.
    6. Kejiang Lei & Dandan Qiu & Shilong Zhang & Zichao Wang & Yan Jin, 2023. "Coal Mine Fire Emergency Rescue Capability Assessment and Emergency Disposal Research," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    7. Leping He & Tao Tang & Qijun Hu & Qijie Cai & Zhijun Li & Shaowu Tang & Yichun Wang, 2021. "Integration of Interpretive Structural Modeling with Fuzzy Bayesian Network for Risk Assessment of Tunnel Collapse," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, December.
    8. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    Full references (including those not matched with items on IDEAS)

    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. Guo, Tianjiao & Geng, Yong & Song, Xiaoqian & Rui, Xue & Ge, Zewen, 2023. "Tracing magnesium flows in China: A dynamic material flow analysis," Resources Policy, Elsevier, vol. 83(C).
    2. Zhang, Yan & Wang, Yu-Hao & Zhao, Xu & Tong, Rui-Peng, 2023. "Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario," Resources Policy, Elsevier, vol. 85(PB).
    3. Bhuyan, Kasturi & Sharma, Hrishikesh, 2024. "Probabilistic capacity models and fragility estimate for NRC and UHSC panels subjected to contact blast," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Liu, Jinbiao & Tan, Lingling & Ma, Yaping, 2024. "An integrated risk assessment method for urban areas due to chemical leakage accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    5. Yingxiu Zhao & Sitong Zhou, 2023. "The Impact of Two-Sided Market Platforms on Participants’ Trading Strategies: An Evolutionary Game Analysis," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
    6. 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).
    7. Wang, Hong & Chen, Ning & Wu, Bing & Guedes Soares, C., 2024. "Human and organizational factors analysis of collision accidents between merchant ships and fishing vessels based on HFACS-BN model," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    8. Wenqi Cui & Xinwu Chen & Weisong Li & Kunjing Li & Kaiwen Liu & Zhanyun Feng & Jiale Chen & Yueling Tian & Boyu Chen & Xianfeng Chen & Wei Cui, 2024. "Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network," Sustainability, MDPI, vol. 16(18), pages 1-20, September.
    9. Wang, Jinpei & Bai, Xuejie & Liu, Yankui, 2023. "Globalized robust bilevel optimization model for hazmat transport network design considering reliability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    11. Li, Wanzhen & Zhou, Lujie & Hao, Jian & Yu, Kai & Chen, Jing & Liu, Pingping & Feng, Rui, 2023. "Dynamic simulation and control strategy exploration of the unsafe behavior of coal mine employees," Resources Policy, Elsevier, vol. 86(PA).
    12. Chen, Xing-lin & Huang, Zong-hou & Ge, Fan-liang & Lin, Wei-dong & Yang, Fu-qiang, 2024. "A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    13. Zerouali, Bilal & Sahraoui, Yacine & Nahal, Mourad & Chateauneuf, Alaa, 2024. "Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    14. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Yu, Hui & Li, Ying & Wang, Wei, 2023. "Optimal innovation strategies of automakers with market competition under the dual-credit policy," Energy, Elsevier, vol. 283(C).
    16. Zhang, Hengqi & Geng, Hua & Zeng, Huarong & Jiang, Li, 2023. "Dynamic risk evaluation and control of electrical personal accidents," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    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:gam:jmathe:v:12:y:2024:i:7:p:1109-:d:1371484. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.