Advertisement

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

  • Guang-Jun Jiang
  • Hong-Xia ChenEmail author
  • Le GaoEmail author
  • Hong-Hua Sun
  • Qing-Yang Li
S.I.: Reliability Modeling with Applications Based on Big Data
  • 10 Downloads

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.

Keywords

Dynamic fault tree Bayesian network Junction tree algorithm Markov 

List of symbols

CSP

Cold spare parts

WSP

Warm spare parts

HSP

Hot spare parts

FDEP

Function dependency gate

SEQ

Sequence enforcing gate

PAND

Priority-AND gate

DAG

Directed acyclic graph

MPD

Marginal probability distribution

CPD

Conditional probability distribution

PPD

Posterior probability distribution

Ai

Input event

B

Input event

T

Trigger event

A

Coefficient

\( \varLambda \)

Failure probability

Tr

Random variable

\( P\left( \bullet \right) \)

The CPD of event

Ci

Base event

D

Intermediate events

F

Top event

Xi

Base event

Ni

Intermediate events

M1

Intermediate events

M2

Main part

M3

Spare part

T

Top event

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under the contract number 71761030 and the Graduate Teaching Program of Inner Mongolia University of Technology under the contract number YJG2017013.

References

  1. Baloglu, U. B., & Demir, Y. (2018). An agent-based Pythagorean fuzzy approach for demand analysis with incomplete information. International Journal of Intelligent Systems, 33(5), 983–997.CrossRefGoogle Scholar
  2. Barua, S., Gao, X. D., Pasman, H., & Mannan, M. S. (2016). Bayesian network based dynamic operational risk assessment. Journal of Loss Prevention in the Process Industries, 41, 399–410.CrossRefGoogle Scholar
  3. Chen, J. Q., Zhao, S. Q., Ma, Y. F., & Hu, Y. Q. (2015). Distribution network reliability assessment based on Bayesian network and blind number. Electric Power Automation Equipment, 35(6), 112–116.Google Scholar
  4. Cou, H. X., An, Z. W., Liu, B., & Gao, J. X. (2016). Reliability analysis of wind turbine gearbox based on Bayesian network. Journal of Lanzhou University of Technology, 42(1), 40–45.Google Scholar
  5. Gu, C. Q., Zhang, C. K., Zhou, D. Y., & Feng, Q. (2014). Reliability analysis of multi-state systems based on intuitionistic fuzzy Bayesian networks. Journal of Northwestern Polytechnical University, 32(5), 744–748.Google Scholar
  6. Huang, H. Z., Li, Y. F., Sun, J., Yang, Y. J., & Xiao, N. C. (2013). Fuzzy dynamic fault tree analysis for the solar array drive assembly. Journal of Mechanical Engineering, 49(19), 7–16.Google Scholar
  7. Li, J. P., Hua, C. C., Yang, Y. N., & Guan, X. P. (2018a). Bayesian block structure sparse based T-S fuzzy modeling for dynamic prediction of hot metal silicon content in the blast furnace. IEEE Transactions on Industrial Electronics, 65(6), 4933–4942.CrossRefGoogle Scholar
  8. Li, X. Y., Huang, H. Z., & Li, Y. F. (2018b). Reliability analysis of phased mission system with non-exponential and partially repairable components. Reliability Engineering & System Safety, 175, 119–127.CrossRefGoogle Scholar
  9. Li, H., Huang, H. Z., Li, Y. F., Zhou, J., & Mi, J. (2018c). Physics of failure-based reliability prediction of turbine blades using multi-source information fusion. Applied Soft Computing, 72, 624–635.CrossRefGoogle Scholar
  10. Li, Y. F., Huang, H. Z., Liu, Y., Xiao, N. C., & Zhu, S. P. (2012). System reliability modeling and assessment of satellite solar array drive assembly based on a Bayesian network. China Science Paper, 7(8), 583–588.Google Scholar
  11. Li, Y. F., Mi, J., Liu, Y., Yang, Y. J., Huang, H. Z. (2015). Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers. Proceedings of the Institution of Mechanical Engineers, Part O, Journal of Risk and Reliability, 229(6), 530–541.Google Scholar
  12. Li, Y. L., Wang, X. J., Wang, C., Xu, M. H., & Wang, L. (2018d). Non-probabilistic Bayesian update method for model validation. Applied Mathematical Modelling, 58, 388–403.CrossRefGoogle Scholar
  13. Liang, X. F., Wang, H. D., Yi, H., & Li, D. (2017). Warship reliability evaluation based on dynamic Bayesian networks and numerical simulation. Ocean Engineering, 136, 129–140.CrossRefGoogle Scholar
  14. Liu, Y. H., Yu, Z. W., Zeng, M., & Zhang, Y. S. (2016). LLE for submersible plunger pump fault diagnosis via joint wavelet and SVD approach. Neurocomputing, 185, 202–211.CrossRefGoogle Scholar
  15. Mi, J., Li, Y. F., Peng, W., & Huang, H. Z. (2018). Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliability Engineering & System Safety, 174, 71–81.CrossRefGoogle Scholar
  16. Mi, J., Li, Y. F., Yang, Y. J., Peng, W., & Huang, H. Z. (2016). Reliability assessment of complex electromechanical systems under epistemic uncertainty. Reliability Engineering & System Safety, 152, 1–15.CrossRefGoogle Scholar
  17. Peng, W. W., Huang, H. Z., Li, Y. F., Yang, Y. J., & Li, H. Q. (2014). Bayesian information fusion method for reliability assessment of milling head. Journal of Mechanical Engineering, 50(6), 185–191.CrossRefGoogle Scholar
  18. Ren, L. N., Wang, Z. M., & Lei, C. L. (2016). Comprehensive evaluation approach to Bayesian reliability assessment model of NC machine tools. Journal of Shanghai Jiaotong University, 50(7), 1023–1029.Google Scholar
  19. Tian, Y., Ren, Q. W., Yang, Y., & Xiong, Y. (2017). A methodology for assessing the reliability and security of cascade reservoir systems. Journal of Risk and Reliability, 231(6), 680–690.Google Scholar
  20. Tu, J. L., Tao, Q. X., Cheng, R. F., & Feng, L. Q. (2017). Dynamic reliability evaluation for integrated modular avionics system based on continuous time Bayesian network with triangular fuzzy numbers. Journal of Aeronautics, Astronautics and Aviation, Series A, 49(2), 159–170.Google Scholar
  21. Wang, X. M., Li, Y. F., Li, A. F., Mi, J. H., & Huang, H. Z. (2015). Reliability modeling and evaluation for rectifier feedback system based on continuous time Bayesian networks under fuzzy numbers. Journal of Mechanical Engineering, 51(14), 167–174.CrossRefGoogle Scholar
  22. Wang, H., Liu, F., & Wang, H. Q. (2014). Research on inference and forecasting technology for petrochemical equipment failure based on Bayesian method. Journal of Safety and Environment, 14(6), 5–7.Google Scholar
  23. Wang, L. Z., Pan, R., Wang, X. H., Fan, W. H., & Xuan, J. Q. (2017). A Bayesian reliability evaluation method with different types of data from multiple sources. Reliability Engineering and System Safety, 167, 128–135.CrossRefGoogle Scholar
  24. Wang, Z. M., & Yang, J. G. (2014a). Bayesian reliability analysis for numerical control machine tools with small-sized sample failure data. Journal of Central South University of Science and Technology, 45(12), 4201–4205.Google Scholar
  25. Wang, Z. M., & Yang, J. G. (2014b). Bayesian reliability assessment for numerically controlled machine tools with imperfect repair. Journal of Shanghai Jiaotong University, 48(5), 614–617.Google Scholar
  26. Yao, C. Y., Chen, D. N., & Wang, B. (2014). Fuzzy reliability assessment method based on T-S fault tree and bayesian network. Journal of Mechanical Engineering, 50(2), 193–201.CrossRefGoogle Scholar
  27. Zhang, Z. Y., Chen, Y. Q., Zhang, H. J., & Wu, W. (2013). Reliability analysis of missile autopilot system based on dynamic fault tree. Journal of Nanjing University of Science and Technology, 37(4), 543–550.Google Scholar
  28. Zhang, X., Gao, H., Huang, H. Z., Li, Y. F., & Mi, J. (2018). Dynamic reliability modeling for system analysis under complex load. Reliability Engineering & System Safety, 180, 345–351.CrossRefGoogle Scholar
  29. Zhang, Y. P., Wang, F., Zhang, S., & Lan, L. (2015). Dependability assessment of railway time synchronization network based on fuzzy Bayesian network. Journal of The China Railway Society, 37(5), 57–63.Google Scholar
  30. Zhang, Y. P., & Yang, J. F. (2017). Reliability analysis on ATP system of CTCS-3 based on dynamic Bayesian network. Journal of The China Railway Society, 39(7), 79–86.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringInner Mongolia University of TechnologyHohhotPeople’s Republic of China

Personalised recommendations