IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v311y2022i1d10.1007_s10479-019-03230-1.html
   My bibliography  Save this article

Reliability analysis on ammonium nitrate/fuel oil explosive vehicle pharmaceutical system based on dynamic fault tree and Bayesian network

Author

Listed:
  • Guang-Jun Jiang

    (Inner Mongolia University of Technology)

  • Hong-Xia Chen

    (Inner Mongolia University of Technology)

  • Le Gao

    (Inner Mongolia University of Technology)

  • Hong-Hua Sun

    (Inner Mongolia University of Technology)

  • Qing-Yang Li

    (Inner Mongolia University of Technology)

Abstract

Ammonium nitrate/fuel oil (ANFO) explosive vehicle is the most common equipment in the mining machinery. The pharmaceutical system is the most important system of the whole mechanical system, its performance and reliability determine the reliability of the whole system. However, there is little literature on reliability analysis of ANFO explosive vehicle. In this paper, ANFO explosive vehicle pharmaceutical system is regarded as the research object. The pharmaceutical system presents complex dynamic characteristics. The electronic control subsystem, for example, contains function dependency gate, and the sensitizer system includes warm spare parts. In this paper, the related theories and methods of dynamic fault tree and Bayesian network were applied to the reliability research of the pharmaceutical system. By analyzing the principle of the system, dynamic fault tree model was established. By mapping relation, it was transformed into Bayesian network. The marginal probability distribution and the conditional probability distribution of each node are determined, and the prior probabilities and the posterior probabilities of the pharmaceutical system are obtained. Compared with the results of Markov’s analysis, there are some deviations between them. According to the sequencing result of the influence degree of parts on the reliability, the fuel flow-meter, oil filter element, sensitizer flow-meter, line interface, and oil pipe are the weak links of the pharmaceutical system. They can be regarded as important objects of system improvement, fault maintenance and health management. The sensitizer system is the most reliable subsystem of the pharmaceutical system. Because of the existence of spare part logic in this subsystem, it has the least influence on the pharmaceutical system. In addition, it is necessary to reduce the impact of the operating environment in the reliability analysis of similar systems. The result can improve maintenance efficiency and provide theoretical support for the system improvement.

Suggested Citation

  • Guang-Jun Jiang & Hong-Xia Chen & Le Gao & Hong-Hua Sun & Qing-Yang Li, 2022. "Reliability analysis on ammonium nitrate/fuel oil explosive vehicle pharmaceutical system based on dynamic fault tree and Bayesian network," Annals of Operations Research, Springer, vol. 311(1), pages 167-182, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03230-1
    DOI: 10.1007/s10479-019-03230-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03230-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-019-03230-1?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. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    2. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    3. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
    4. Wang, Lizhi & Pan, Rong & Wang, Xiaohong & Fan, Wenhui & Xuan, Jinquan, 2017. "A Bayesian reliability evaluation method with different types of data from multiple sources," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 128-135.
    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. Yuan-Jian Yang & Ya-Lan Xiong & Xin-Yin Zhang & Gui-Hua Wang & Bihai Zou, 2022. "Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks," Annals of Operations Research, Springer, vol. 311(1), pages 451-467, April.
    2. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    3. Ying-Kui Gu & Chao-Jun Fan & Ling-Qiang Liang & Jun Zhang, 2022. "Reliability calculation method based on the Copula function for mechanical systems with dependent failure," Annals of Operations Research, Springer, vol. 311(1), pages 99-116, April.
    4. Yan-Feng Li & Hong-Zhong Huang & Jinhua Mi & Weiwen Peng & Xiaomeng Han, 2022. "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability," Annals of Operations Research, Springer, vol. 311(1), pages 195-209, April.
    5. Jie Zhou & Hong-Zhong Huang & Yan-Feng Li & Junyu Guo, 2022. "A framework for fatigue reliability analysis of high-pressure turbine blades," Annals of Operations Research, Springer, vol. 311(1), pages 489-505, April.
    6. Jianxiong Gao & Zongwen An & Xuezong Bai, 2022. "A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model," Annals of Operations Research, Springer, vol. 311(1), pages 81-97, April.
    7. Zheng Liu & Xin Liu & Hong-Zhong Huang & Pingyu Zhu & Zhongwei Liang, 2022. "A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle," Annals of Operations Research, Springer, vol. 311(1), pages 295-310, April.
    8. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Wang, Ning, 2024. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    10. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    11. Zeng, Ying & Huang, Tudi & Li, Yan-Feng & Huang, Hong-Zhong, 2023. "Reliability modeling for power converter in satellite considering periodic phased mission," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    12. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    13. Yong-Hua Li & Fu-Yu Zhao & Yue-Hua Gao & Peng-Peng Zhi, 2022. "Importance analysis of underframe connection system for the pantograph lower arm rod," Annals of Operations Research, Springer, vol. 311(1), pages 211-223, April.
    14. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    15. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    16. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xianqi, 2019. "Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 123-142.
    17. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    18. Li, Xiang-Yu & Xiong, Xiaoyan & Guo, Junyu & Huang, Hong-Zhong & Li, Xiaopeng, 2022. "Reliability assessment of non-repairable multi-state phased mission systems with backup missions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    19. Li, Xiang-Yu & Li, Yan-Feng & Huang, Hong-Zhong & Zio, Enrico, 2018. "Reliability assessment of phased-mission systems under random shocks," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 352-361.
    20. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.

    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:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03230-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.