Reliability Analysis of Dragline Subsystem using Bayesian Network Approach

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Authors

  • Deepak Kumar
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi – 221005, Uttar Pradesh ,IN
  • Debasis Jana
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi – 221005, Uttar Pradesh ,IN
  • Pawan Kumar Yadav
     Department of Mechanical Engineering, RIMT University, Gobindgarh – 147301, Punjab ,IN
  • Suprakash Gupta
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi – 221005, Uttar Pradesh ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/31958

Keywords:

Dragline, Bayesian Network, Fault Tree Analysis, Reliability Analysis

Abstract

Ensuring high reliability and availability of draglines is imperative for the economic sustainability of a highly productive surface mining project. Draglines are very complex in design and consist of hundreds of components. Reliability modelling of a large complex system is difficult with conventional reliability analysis techniques. The dragging mechanism is a critical subsystem for the smooth operation of the draglines. This study uses the Bayesian Network (BN) model, mapped from the Fault Tree (FT), for the reliability analysis of Dragline. Sensitivity analysis identifies the critical components – helpful information for reliability management. The results demonstrate that three components of the dragging mechanism, namely, the drag motor system, drag brake and drag socket are primarily responsible for the poor reliability of the case study system. This study provides valuable information for maintenance planning of operating draglines and reliability blueprint of future dragline design.

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Published

2022-11-25

How to Cite

Deepak Kumar, Debasis Jana, Pawan Kumar Yadav, & Suprakash Gupta. (2022). Reliability Analysis of Dragline Subsystem using Bayesian Network Approach. Journal of Mines, Metals and Fuels, 70(7), 341–353. https://doi.org/10.18311/jmmf/2022/31958

Issue

Volume 70, Issue 7, July 2022

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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