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A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining

Author

Listed:
  • Prerita Odeyar

    (School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Derek B. Apel

    (School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Robert Hall

    (Department of Mining Engineering and Management (MEM), South Dakota School of Mines, Rapid City, SD 57701, USA)

  • Brett Zon

    (North American Construction Group, 27287-100 Avenue, Acheson, AB T7X 6H8, Canada)

  • Krzysztof Skrzypkowski

    (Faculty of Civil Engineering and Resource Management, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

To achieve a targeted production level in mining industries, all machine systems and their subsystems must perform efficiently and be reliable during their lifetime. Implications of equipment failure have become more critical with the increasing size and intricacy of the machinery. Appropriate maintenance planning reduces the overall maintenance cost, increases machine life, and results in optimized life cycle costs. Several techniques have been used in the past to predict reliability, and there’s always been scope for improvement of the same. Researchers are finding new methods for better analysis of faults and reliability from traditional statistical methods to applying artificial intelligence. With the advancement of Industry 4.0, the mining industry is steadily moving towards the predictive maintenance approach to correct potential faults and increase equipment reliability. This paper attempts to provide a comprehensive review of different statistical techniques that have been applied for reliability and fault prediction from both theoretical aspects and industrial applications. Further, the advantages and limitations of the algorithm are discussed, and the efficiency of new ML methods are compared to the traditional methods used.

Suggested Citation

  • Prerita Odeyar & Derek B. Apel & Robert Hall & Brett Zon & Krzysztof Skrzypkowski, 2022. "A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining," Energies, MDPI, vol. 15(17), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6263-:d:899738
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    References listed on IDEAS

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    1. Barabady, Javad & Kumar, Uday, 2008. "Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 647-653.
    2. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    3. Guang-Jun Jiang & Zong-Yuan Li & Guan Qiao & Hong-Xia Chen & Hai-Bin Li & Hong-Hua Sun, 2021. "Reliability Analysis of Dynamic Fault Tree Based on Binary Decision Diagrams for Explosive Vehicle," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, April.
    4. Balbir S. Dhillon, 2008. "Mining Equipment Reliability, Maintainability, and Safety," Springer Series in Reliability Engineering, Springer, number 978-1-84800-288-3, December.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
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    4. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
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    6. Mohammad Alhusban & Mohannad Alhusban & Ayah A. Alkhawaldeh, 2023. "The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering," Sustainability, MDPI, vol. 16(1), pages 1-32, December.
    7. Hossein Abbaspour & Carsten Drebenstedt, 2023. "Truck–Shovel vs. In-Pit Crushing and Conveying Systems in Open Pit Mines: A Technical Evaluation for Selecting the Most Effective Transportation System by System Dynamics Modeling," Logistics, MDPI, vol. 7(4), pages 1-15, December.
    8. Kartick Bhushan & Somnath Chattopadhyaya & Shubham Sharma & Kamal Sharma & Changhe Li & Yanbin Zhang & Elsayed Mohamed Tag Eldin, 2022. "Analyzing Reliability and Maintainability of Crawler Dozer BD155 Transmission Failure Using Markov Method and Total Productive Maintenance: A Novel Case Study for Improvement Productivity," Sustainability, MDPI, vol. 14(21), pages 1-17, November.

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