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Bayesian Network Approach for Dragline Reliability Analysis: a Case Study

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Abstract

Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance.

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Acknowledgements

The authors gratefully acknowledge the reviewers for providing fruitful reviews. The authors are also thankful to Prof. Sukumar Bandopadhyay (Professor Emeritus, Department of Mining Engineering, UAF, Alaska) for editing and improving the presentation of the manuscript.

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Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, U.P., India, 221005

    Deepak Kumar, Debasis Jana & Suprakash Gupta

  2. Department of Mechanical Engineering, RIMT University, Punjab, India

    Pawan Kumar Yadav

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  1. Deepak Kumar
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  2. Debasis Jana
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  3. Suprakash Gupta
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  4. Pawan Kumar Yadav
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Correspondence to Deepak Kumar.

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Kumar, D., Jana, D., Gupta, S. et al. Bayesian Network Approach for Dragline Reliability Analysis: a Case Study. Mining, Metallurgy & Exploration 40, 347–365 (2023). https://doi.org/10.1007/s42461-023-00729-x

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